Appendix A — Recommended reading

For each chapter, about six papers are recommended for reading in full. Most of them come from the reading lists used for the exam in earlier years. They are listed here with their titles, abstracts and links. In the PDF version the open-access papers follow each list in full; the others are available with your SU login through the library link.

A.1 Sequence searching

Recommended reading for Chapter 1 (listed in Section 1.13).

These papers are recommended for reading in full; most come from the reading lists used in earlier years. Open-access papers are included; the others are available with your SU login through the library link.

Smith and Waterman (1981) — The local alignment algorithm, in three pages.
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Identification of common molecular subsequences. Smith & Waterman (1981). Journal of Molecular Biology. doi:10.1016/0022-2836(81)90087-5
Altschul et al. (1997) — Gapped BLAST and PSI-BLAST; still the standard reference for heuristic search and profiles.
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Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Altschul et al. (1997). Nucleic Acids Research. doi:10.1093/nar/25.17.3389

Abstract

The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSI-BLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.
Eddy (2011) — HMMER3; profile HMM search made as fast as BLAST, with a clear account of the statistics.
PDF · DOI · open access (CC BY 4.0)

Accelerated Profile HMM Searches. Eddy (2011). PLoS Computational Biology. doi:10.1371/journal.pcbi.1002195

Abstract

Profile hidden Markov models (profile HMMs) and probabilistic inference methods have made important contributions to the theory of sequence database homology search. However, practical use of profile HMM methods has been hindered by the computational expense of existing software implementations. Here I describe an acceleration heuristic for profile HMMs, the "multiple segment Viterbi" (MSV) algorithm. The MSV algorithm computes an optimal sum of multiple ungapped local alignment segments using a striped vector-parallel approach previously described for fast Smith/Waterman alignment. MSV scores follow the same statistical distribution as gapped optimal local alignment scores, allowing rapid evaluation of significance of an MSV score and thus facilitating its use as a heuristic filter. I also describe a 20-fold acceleration of the standard profile HMM Forward/Backward algorithms using a method I call "sparse rescaling". These methods are assembled in a pipeline in which high-scoring MSV hits are passed on for reanalysis with the full HMM Forward/Backward algorithm. This accelerated pipeline is implemented in the freely available HMMER3 software package. Performance benchmarks show that the use of the heuristic MSV filter sacrifices negligible sensitivity compared to unaccelerated profile HMM searches. HMMER3 is substantially more sensitive and 100- to 1000-fold faster than HMMER2. HMMER3 is now about as fast as BLAST for protein searches.
Steinegger and Söding (2017) — MMseqs2; how sensitive search scales to billions of sequences.
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MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Steinegger & Söding (2017). Nature Biotechnology. doi:10.1038/nbt.3988
van Kempen et al. (2023) — Foldseek; searching with structure instead of sequence.
PDF · DOI · open access (CC BY 4.0)

Fast and accurate protein structure search with Foldseek. van Kempen et al. (2023). Nature Biotechnology. doi:10.1038/s41587-023-01773-0

Abstract

As structure prediction methods are generating millions of publicly available protein structures, searching these databases is becoming a bottleneck. Foldseek aligns the structure of a query protein against a database by describing tertiary amino acid interactions within proteins as sequences over a structural alphabet. Foldseek decreases computation times by four to five orders of magnitude with 86%, 88% and 133% of the sensitivities of Dali, TM-align and CE, respectively.

A.2 Databases

Recommended reading for Chapter 2 (listed in Section 2.11).

Bateman et al. (2024) — UniProt today; what Swiss-Prot, TrEMBL, UniParc and UniRef are, and how annotation is done.
PDF · DOI · open access (CC BY 4.0)

UniProt: the Universal Protein Knowledgebase in 2025. Bateman et al. (2024). Nucleic Acids Research. doi:10.1093/nar/gkae1010

Abstract

The aim of the UniProt Knowledgebase (UniProtKB; https://www.uniprot.org/) is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this publication, we describe ongoing changes to our production pipeline to limit the sequences available in UniProtKB to high-quality, non-redundant reference proteomes. We continue to manually curate the scientific literature to add the latest functional data and use machine learning techniques. We also encourage community curation to ensure key publications are not missed. We provide an update on the automatic annotation methods used by UniProtKB to predict information for unreviewed entries describing unstudied proteins. Finally, updates to the UniProt website are described, including a new tab linking protein to genomic information. In recognition of its value to the scientific community, the UniProt database has been awarded Global Core Biodata Resource status.
Blum et al. (2024) — InterPro, the integrated classification of protein families, domains and sites.
PDF · DOI · open access (CC BY 4.0)

InterPro: the protein sequence classification resource in 2025. Blum et al. (2024). Nucleic Acids Research. doi:10.1093/nar/gkae1082

Abstract

InterPro (https://www.ebi.ac.uk/interpro) is a freely accessible resource for the classification of protein sequences into families. It integrates predictive models, known as signatures, from multiple member databases to classify sequences into families and predict the presence of domains and significant sites. The InterPro database provides annotations for over 200 million sequences, ensuring extensive coverage of UniProtKB, the standard repository of protein sequences, and includes mappings to several other major resources, such as Gene Ontology (GO), Protein Data Bank in Europe (PDBe) and the AlphaFold Protein Structure Database. In this publication, we report on the status of InterPro (version 101.0), detailing new developments in the database, associated web interface and software. Notable updates include the increased integration of structures predicted by AlphaFold and the enhanced description of protein families using artificial intelligence. Over the past two years, more than 5000 new InterPro entries have been created. The InterPro website now offers access to 85 000 protein families and domains from its member databases and serves as a long-term archive for retired databases. InterPro data, software and tools are freely available.
Burley et al. (2018) — the Protein Data Bank as a single global archive, and how structures are deposited and validated.
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Protein Data Bank: the single global archive for 3D macromolecular structure data. Burley et al. (2018). Nucleic Acids Research. doi:10.1093/nar/gky949

Abstract

The Protein Data Bank (PDB) is the single global archive of experimentally determined three-dimensional (3D) structure data of biological macromolecules. Since 2003, the PDB has been managed by the Worldwide Protein Data Bank (wwPDB; wwpdb.org), an international consortium that collaboratively oversees deposition, validation, biocuration, and open access dissemination of 3D macromolecular structure data. The PDB Core Archive houses 3D atomic coordinates of more than 144 000 structural models of proteins, DNA/RNA, and their complexes with metals and small molecules and related experimental data and metadata. Structure and experimental data/metadata are also stored in the PDB Core Archive using the readily extensible wwPDB PDBx/mmCIF master data format, which will continue to evolve as data/metadata from new experimental techniques and structure determination methods are incorporated by the wwPDB. Impacts of the recently developed universal wwPDB OneDep deposition/validation/biocuration system and various methods-specific wwPDB Validation Task Forces on improving the quality of structures and data housed in the PDB Core Archive are described together with current challenges and future plans.
Bertoni et al. (2025) — the AlphaFold Protein Structure Database and how to use predicted structures.
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AlphaFold Protein Structure Database 2025: a redesigned interface and updated structural coverage. Bertoni et al. (2025). Nucleic Acids Research. doi:10.1093/nar/gkaf1226

Abstract

The AlphaFold Protein Structure Database (AFDB; https://alphafold.ebi.ac.uk), developed by EMBL-EBI and Google DeepMind, provides open access to hundreds of millions of high-accuracy protein structure predictions, transforming research in structural biology and the wider life sciences. Since its launch, AFDB has become a widely used bioinformatics resource, integrated into major databases, visualization platforms, and analysis pipelines. Here, we report the update of the database to align with the UniProt 2025_03 release, along with a comprehensive redesign of the entry page to enhance usability, accessibility, and structural interpretation. The new design integrates annotations directly with an interactive 3D viewer and introduces dedicated domains and summary tabs. Structural coverage has also been updated to include isoforms plus underlying multiple sequence alignments. Data are available through the website, FTP, Google Cloud, and updated APIs. Together, these advances reinforce AFDB as a sustainable resource for exploring protein sequence-structure relationships.
Szklarczyk et al. (2024) — STRING; how functional association networks are built and scored.
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The STRING database in 2025: protein networks with directionality of regulation. Szklarczyk et al. (2024). Nucleic Acids Research. doi:10.1093/nar/gkae1113

Abstract

Proteins cooperate, regulate and bind each other to achieve their functions. Understanding the complex network of their interactions is essential for a systems-level description of cellular processes. The STRING database compiles, scores and integrates protein-protein association information drawn from experimental assays, computational predictions and prior knowledge. Its goal is to create comprehensive and objective global networks that encompass both physical and functional interactions. Additionally, STRING provides supplementary tools such as network clustering and pathway enrichment analysis. The latest version, STRING 12.5, introduces a new 'regulatory network', for which it gathers evidence on the type and directionality of interactions using curated pathway databases and a fine-tuned language model parsing the literature. This update enables users to visualize and access three distinct network types-functional, physical and regulatory-separately, each applicable to distinct research needs. In addition, the pathway enrichment detection functionality has been updated, with better false discovery rate corrections, redundancy filtering and improved visual displays. The resource now also offers improved annotations of clustered networks and provides users with downloadable network embeddings, which facilitate the use of STRING networks in machine learning and allow cross-species transfer of protein information. The STRING database is available online at https://string-db.org/.
The Gene Ontology Consortium et al. (2023) — the Gene Ontology knowledgebase; terms, annotations and evidence.
PDF · DOI · open access (CC BY 4.0)

The Gene Ontology knowledgebase in 2023. The Gene Ontology Consortium et al. (2023). GENETICS. doi:10.1093/genetics/iyad031

Abstract

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.

A.3 Evolution

Recommended reading for Chapter 3 (listed in Section 3.9).

Kimura (1968) — the neutral theory in two pages; why most molecular change may be neutral.
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Evolutionary Rate at the Molecular Level. Kimura (1968). Nature. doi:10.1038/217624a0
Koonin (2005) — orthologues, paralogues and evolutionary genomics; the definitions and why they matter.
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Orthologs, Paralogs, and Evolutionary Genomics. Koonin (2005). Annual Review of Genetics. doi:10.1146/annurev.genet.39.073003.114725

Abstract

Orthologs and paralogs are two fundamentally different types of homologous genes that evolved, respectively, by vertical descent from a single ancestral gene and by duplication. Orthology and paralogy are key concepts of evolutionary genomics. A clear distinction between orthologs and paralogs is critical for the construction of a robust evolutionary classification of genes and reliable functional annotation of newly sequenced genomes. Genome comparisons show that orthologous relationships with genes from taxonomically distant species can be established for the majority of the genes from each sequenced genome. This review examines in depth the definitions and subtypes of orthologs and paralogs, outlines the principal methodological approaches employed for identification of orthology and paralogy, and considers evolutionary and functional implications of these concepts.
Lynch and Conery (2000) — how often genes duplicate and what happens to the copies.
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The Evolutionary Fate and Consequences of Duplicate Genes. Lynch & Conery (2000). Science. doi:10.1126/science.290.5494.1151

Abstract

Gene duplication has generally been viewed as a necessary source of material for the origin of evolutionary novelties, but it is unclear how often gene duplicates arise and how frequently they evolve new functions. Observations from the genomic databases for several eukaryotic species suggest that duplicate genes arise at a very high rate, on average 0.01 per gene per million years. Most duplicated genes experience a brief period of relaxed selection early in their history, with a moderate fraction of them evolving in an effectively neutral manner during this period. However, the vast majority of gene duplicates are silenced within a few million years, with the few survivors subsequently experiencing strong purifying selection. Although duplicate genes may only rarely evolve new functions, the stochastic silencing of such genes may play a significant role in the passive origin of new species.
Echave et al. (2016) — why rates of evolution vary among protein sites; the link between structure and evolution.
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Causes of evolutionary rate variation among protein sites. Echave et al. (2016). Nature Reviews Genetics. doi:10.1038/nrg.2015.18

Abstract

It has long been recognized that certain sites within a protein, such as sites in the protein core or catalytic residues in enzymes, are evolutionarily more conserved than other sites. However, our understanding of rate variation among sites remains surprisingly limited. Recent progress to address this includes the development of a wide array of reliable methods to estimate site-specific substitution rates from sequence alignments. In addition, several molecular traits have been identified that correlate with site-specific mutation rates, and novel mechanistic biophysical models have been proposed to explain the observed correlations. Nonetheless, current models explain, at best, approximately 60% of the observed variance, highlighting the limitations of current methods and models and the need for new research directions.
Treangen and Rocha (2011) — horizontal transfer, not duplication, drives gene family expansion in prokaryotes.
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Horizontal Transfer, Not Duplication, Drives the Expansion of Protein Families in Prokaryotes. Treangen & Rocha (2011). PLoS Genetics. doi:10.1371/journal.pgen.1001284

Abstract

Gene duplication followed by neo- or sub-functionalization deeply impacts the evolution of protein families and is regarded as the main source of adaptive functional novelty in eukaryotes. While there is ample evidence of adaptive gene duplication in prokaryotes, it is not clear whether duplication outweighs the contribution of horizontal gene transfer in the expansion of protein families. We analyzed closely related prokaryote strains or species with small genomes (Helicobacter, Neisseria, Streptococcus, Sulfolobus), average-sized genomes (Bacillus, Enterobacteriaceae), and large genomes (Pseudomonas, Bradyrhizobiaceae) to untangle the effects of duplication and horizontal transfer. After removing the effects of transposable elements and phages, we show that the vast majority of expansions of protein families are due to transfer, even among large genomes. Transferred genes--xenologs--persist longer in prokaryotic lineages possibly due to a higher/longer adaptive role. On the other hand, duplicated genes--paralogs--are expressed more, and, when persistent, they evolve slower. This suggests that gene transfer and gene duplication have very different roles in shaping the evolution of biological systems: transfer allows the acquisition of new functions and duplication leads to higher gene dosage. Accordingly, we show that paralogs share most protein-protein interactions and genetic regulators, whereas xenologs share very few of them. Prokaryotes invented most of life's biochemical diversity. Therefore, the study of the evolution of biology systems should explicitly account for the predominant role of horizontal gene transfer in the diversification of protein families.
Emms and Kelly (2019) — OrthoFinder; how orthology is inferred from gene trees at genome scale.
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OrthoFinder: phylogenetic orthology inference for comparative genomics. Emms & Kelly (2019). Genome Biology. doi:10.1186/s13059-019-1832-y

Abstract

Here, we present a major advance of the OrthoFinder method. This extends OrthoFinder's high accuracy orthogroup inference to provide phylogenetic inference of orthologs, rooted gene trees, gene duplication events, the rooted species tree, and comparative genomics statistics. Each output is benchmarked on appropriate real or simulated datasets, and where comparable methods exist, OrthoFinder is equivalent to or outperforms these methods. Furthermore, OrthoFinder is the most accurate ortholog inference method on the Quest for Orthologs benchmark test. Finally, OrthoFinder's comprehensive phylogenetic analysis is achieved with equivalent speed and scalability to the fastest, score-based heuristic methods. OrthoFinder is available at https://github.com/davidemms/OrthoFinder.

A.4 Phylogeny

Recommended reading for Chapter 4 (listed in Section 4.7).

Saitou and Nei (1987) — neighbour joining; the distance method still used everywhere, and a model of a clear algorithm paper.
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The neighbor-joining method: a new method for reconstructing phylogenetic trees. Saitou & Nei (1987). Molecular Biology and Evolution. doi:10.1093/oxfordjournals.molbev.a040454

Abstract

A new method called the neighbor-joining method is proposed for reconstructing phylogenetic trees from evolutionary distance data. The principle of this method is to find pairs of operational taxonomic units (OTUs [= neighbors]) that minimize the total branch length at each stage of clustering of OTUs starting with a starlike tree. The branch lengths as well as the topology of a parsimonious tree can quickly be obtained by using this method. Using computer simulation, we studied the efficiency of this method in obtaining the correct unrooted tree in comparison with that of five other tree-making methods: the unweighted pair group method of analysis, Farris's method, Sattath and Tversky's method, Li's method, and Tateno et al.'s modified Farris method. The new, neighbor-joining method and Sattath and Tversky's method are shown to be generally better than the other methods.
Felsenstein (1981) — maximum likelihood trees from DNA sequences and the pruning algorithm.
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Evolutionary trees from DNA sequences: A maximum likelihood approach. Felsenstein (1981). Journal of Molecular Evolution. doi:10.1007/bf01734359

Abstract

The application of maximum likelihood techniques to the estimation of evolutionary trees from nucleic acid sequence data is discussed. A computationally feasible method for finding such maximum likelihood estimates is developed, and a computer program is available. This method has advantages over the traditional parsimony algorithms, which can give misleading results if rates of evolution differ in different lineages. It also allows the testing of hypotheses about the constancy of evolutionary rates by likelihood ratio tests, and gives rough indication of the error of ;the estimate of the tree.
Kapli et al. (2020) — tree building in the genomic age; an up-to-date review of methods, models and pitfalls.
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Phylogenetic tree building in the genomic age. Kapli et al. (2020). Nature Reviews Genetics. doi:10.1038/s41576-020-0233-0

Abstract

Knowing phylogenetic relationships among species is fundamental for many studies in biology. An accurate phylogenetic tree underpins our understanding of the major transitions in evolution, such as the emergence of new body plans or metabolism, and is key to inferring the origin of new genes, detecting molecular adaptation, understanding morphological character evolution and reconstructing demographic changes in recently diverged species. Although data are ever more plentiful and powerful analysis methods are available, there remain many challenges to reliable tree building. Here, we discuss the major steps of phylogenetic analysis, including identification of orthologous genes or proteins, multiple sequence alignment, and choice of substitution models and inference methodologies. Understanding the different sources of errors and the strategies to mitigate them is essential for assembling an accurate tree of life.
Minh et al. (2020) — IQ-TREE 2; what a modern maximum-likelihood program does (models, search, support).
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IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. Minh et al. (2020). Molecular Biology and Evolution. doi:10.1093/molbev/msaa015

Abstract

IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.
Philippe et al. (2011) — why more sequences are not enough; systematic error in phylogenomics.
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Resolving Difficult Phylogenetic Questions: Why More Sequences Are Not Enough. Philippe et al. (2011). PLoS Biology. doi:10.1371/journal.pbio.1000602
Hug et al. (2016) — a new view of the tree of life from thousands of genomes.
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A new view of the tree of life. Hug et al. (2016). Nature Microbiology. doi:10.1038/nmicrobiol.2016.48

Abstract

The tree of life is one of the most important organizing principles in biology(1). Gene surveys suggest the existence of an enormous number of branches(2), but even an approximation of the full scale of the tree has remained elusive. Recent depictions of the tree of life have focused either on the nature of deep evolutionary relationships(3-5) or on the known, well-classified diversity of life with an emphasis on eukaryotes(6). These approaches overlook the dramatic change in our understanding of life's diversity resulting from genomic sampling of previously unexamined environments. New methods to generate genome sequences illuminate the identity of organisms and their metabolic capacities, placing them in community and ecosystem contexts(7,8). Here, we use new genomic data from over 1,000 uncultivated and little known organisms, together with published sequences, to infer a dramatically expanded version of the tree of life, with Bacteria, Archaea and Eukarya included. The depiction is both a global overview and a snapshot of the diversity within each major lineage. The results reveal the dominance of bacterial diversification and underline the importance of organisms lacking isolated representatives, with substantial evolution concentrated in a major radiation of such organisms. This tree highlights major lineages currently underrepresented in biogeochemical models and identifies radiations that are probably important for future evolutionary analyses.

A.5 Genomics

Recommended reading for Chapter 5 (listed in Section 5.10).

Goodwin et al. (2016) — ten years of next-generation sequencing; how the technologies work and what their errors are.
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Coming of age: ten years of next-generation sequencing technologies. Goodwin et al. (2016). Nature Reviews Genetics. doi:10.1038/nrg.2016.49

Abstract

Since the completion of the human genome project in 2003, extraordinary progress has been made in genome sequencing technologies, which has led to a decreased cost per megabase and an increase in the number and diversity of sequenced genomes. An astonishing complexity of genome architecture has been revealed, bringing these sequencing technologies to even greater advancements. Some approaches maximize the number of bases sequenced in the least amount of time, generating a wealth of data that can be used to understand increasingly complex phenotypes. Alternatively, other approaches now aim to sequence longer contiguous pieces of DNA, which are essential for resolving structurally complex regions. These and other strategies are providing researchers and clinicians a variety of tools to probe genomes in greater depth, leading to an enhanced understanding of how genome sequence variants underlie phenotype and disease.
Compeau et al. (2011) — how to apply de Bruijn graphs to genome assembly; the key algorithmic idea, very clearly explained.
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How to apply de Bruijn graphs to genome assembly. Compeau et al. (2011). Nature Biotechnology. doi:10.1038/nbt.2023
Langmead et al. (2009) — Bowtie; read mapping with the Burrows–Wheeler transform.
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Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Langmead et al. (2009). Genome Biology. doi:10.1186/gb-2009-10-3-r25

Abstract

Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source (http://bowtie.cbcb.umd.edu).
Nielsen et al. (2011) — genotype and SNP calling from sequencing data; the probabilistic framework.
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Genotype and SNP calling from next-generation sequencing data. Nielsen et al. (2011). Nature Reviews Genetics. doi:10.1038/nrg2986

Abstract

Meaningful analysis of next-generation sequencing (NGS) data, which are produced extensively by genetics and genomics studies, relies crucially on the accurate calling of SNPs and genotypes. Recently developed statistical methods both improve and quantify the considerable uncertainty associated with genotype calling, and will especially benefit the growing number of studies using low- to medium-coverage data. We review these methods and provide a guide for their use in NGS studies.
Yandell and Ence (2012) — a beginner’s guide to eukaryotic genome annotation.
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A beginner’s guide to eukaryotic genome annotation. Yandell & Ence (2012). Nature Reviews Genetics. doi:10.1038/nrg3174

Abstract

The falling cost of genome sequencing is having a marked impact on the research community with respect to which genomes are sequenced and how and where they are annotated. Genome annotation projects have generally become small-scale affairs that are often carried out by an individual laboratory. Although annotating a eukaryotic genome assembly is now within the reach of non-experts, it remains a challenging task. Here we provide an overview of the genome annotation process and the available tools and describe some best-practice approaches.
Quince et al. (2017) — shotgun metagenomics from sampling to analysis.
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Shotgun metagenomics, from sampling to analysis. Quince et al. (2017). Nature Biotechnology. doi:10.1038/nbt.3935

Abstract

Diverse microbial communities of bacteria, archaea, viruses and single-celled eukaryotes have crucial roles in the environment and in human health. However, microbes are frequently difficult to culture in the laboratory, which can confound cataloging of members and understanding of how communities function. High-throughput sequencing technologies and a suite of computational pipelines have been combined into shotgun metagenomics methods that have transformed microbiology. Still, computational approaches to overcome the challenges that affect both assembly-based and mapping-based metagenomic profiling, particularly of high-complexity samples or environments containing organisms with limited similarity to sequenced genomes, are needed. Understanding the functions and characterizing specific strains of these communities offers biotechnological promise in therapeutic discovery and innovative ways to synthesize products using microbial factories and can pinpoint the contributions of microorganisms to planetary, animal and human health.

A.6 Genomes

Recommended reading for Chapter 6 (listed in Section 6.11).

International Human Genome Sequencing Consortium et al. (2001) — the human genome draft; what a genome looks like, and how it was obtained.
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Initial sequencing and analysis of the human genome. International Human Genome Sequencing Consortium et al. (2001). Nature. doi:10.1038/35057062

Abstract

The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.
Lynch and Conery (2003) — why prokaryotic and eukaryotic genomes differ; the population-genetic theory of genome complexity.
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The Origins of Genome Complexity. Lynch & Conery (2003). Science. doi:10.1126/science.1089370

Abstract

Complete genomic sequences from diverse phylogenetic lineages reveal notable increases in genome complexity from prokaryotes to multicellular eukaryotes. The changes include gradual increases in gene number, resulting from the retention of duplicate genes, and more abrupt increases in the abundance of spliceosomal introns and mobile genetic elements. We argue that many of these modifications emerged passively in response to the long-term population-size reductions that accompanied increases in organism size. According to this model, much of the restructuring of eukaryotic genomes was initiated by nonadaptive processes, and this in turn provided novel substrates for the secondary evolution of phenotypic complexity by natural selection. The enormous long-term effective population sizes of prokaryotes may impose a substantial barrier to the evolution of complex genomes and morphologies.
McInerney et al. (2017) — why prokaryotes have pan-genomes; core and accessory genes, and adaptive vs. neutral explanations.
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Why prokaryotes have pangenomes. McInerney et al. (2017). Nature Microbiology. doi:10.1038/nmicrobiol.2017.40
Green et al. (2010) — the draft Neanderthal genome and the discovery of admixture with modern humans.
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A Draft Sequence of the Neandertal Genome. Green et al. (2010). Science. doi:10.1126/science.1188021

Abstract

Neandertals, the closest evolutionary relatives of present-day humans, lived in large parts of Europe and western Asia before disappearing 30,000 years ago. We present a draft sequence of the Neandertal genome composed of more than 4 billion nucleotides from three individuals. Comparisons of the Neandertal genome to the genomes of five present-day humans from different parts of the world identify a number of genomic regions that may have been affected by positive selection in ancestral modern humans, including genes involved in metabolism and in cognitive and skeletal development. We show that Neandertals shared more genetic variants with present-day humans in Eurasia than with present-day humans in sub-Saharan Africa, suggesting that gene flow from Neandertals into the ancestors of non-Africans occurred before the divergence of Eurasian groups from each other.
Bourque et al. (2018) — ten things you should know about transposable elements.
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Ten things you should know about transposable elements. Bourque et al. (2018). Genome Biology. doi:10.1186/s13059-018-1577-z

Abstract

Transposable elements (TEs) are major components of eukaryotic genomes. However, the extent of their impact on genome evolution, function, and disease remain a matter of intense interrogation. The rise of genomics and large-scale functional assays has shed new light on the multi-faceted activities of TEs and implies that they should no longer be marginalized. Here, we introduce the fundamental properties of TEs and their complex interactions with their cellular environment, which are crucial to understanding their impact and manifold consequences for organismal biology. While we draw examples primarily from mammalian systems, the core concepts outlined here are relevant to a broad range of organisms.
Eddy (2012) — the C-value paradox, junk DNA and ENCODE, in two pages.
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The C-value paradox, junk DNA and ENCODE. Eddy (2012). Current Biology. doi:10.1016/j.cub.2012.10.002

A.7 Expression analysis

Recommended reading for Chapter 7 (listed in Section 7.9).

Wang et al. (2009) — RNA-seq, a revolutionary tool for transcriptomics; why sequencing replaced arrays.
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RNA-Seq: a revolutionary tool for transcriptomics. Wang et al. (2009). Nature Reviews Genetics. doi:10.1038/nrg2484

Abstract

RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes. RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods. This article describes the RNA-Seq approach, the challenges associated with its application, and the advances made so far in characterizing several eukaryote transcriptomes.
Conesa et al. (2016) — a survey of best practices for RNA-seq data analysis, from design to interpretation.
PDF · DOI · open access (CC BY 4.0)

A survey of best practices for RNA-seq data analysis. Conesa et al. (2016). Genome Biology. doi:10.1186/s13059-016-0881-8

Abstract

RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.
Love et al. (2014) — DESeq2; negative binomial models, dispersion and fold-change shrinkage.
PDF · DOI · open access (CC BY 4.0)

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Love et al. (2014). Genome Biology. doi:10.1186/s13059-014-0550-8

Abstract

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html webcite.
Benjamini and Hochberg (1995) — the false discovery rate; the most important idea in genome-scale statistics.
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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Benjamini & Hochberg (1995). Journal of the Royal Statistical Society Series B: Statistical Methodology. doi:10.1111/j.2517-6161.1995.tb02031.x

Abstract

SUMMARY The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses — the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferronitype procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.
Luecken and Theis (2019) — best practices in single-cell RNA-seq analysis, step by step.
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Current best practices in single-cell RNA-seq analysis: a tutorial. Luecken & Theis (2019). Molecular Systems Biology. doi:10.15252/msb.20188746

Abstract

Single-cell RNA-seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single-cell analysis methods. As more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up-to-date workflow to analyse one's data. Here, we detail the steps of a typical single-cell RNA-seq analysis, including pre-processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell- and gene-level downstream analysis. We formulate current best-practice recommendations for these steps based on independent comparison studies. We have integrated these best-practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at https://www.github.com/theislab/single-cell-tutorial This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.
Nam et al. (2020) — cancer evolution studied with single-cell multi-omics.
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Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics. Nam et al. (2020). Nature Reviews Genetics. doi:10.1038/s41576-020-0265-5

Abstract

Cancer represents an evolutionary process through which growing malignant populations genetically diversify, leading to tumour progression, relapse and resistance to therapy. In addition to genetic diversity, the cell-to-cell variation that fuels evolutionary selection also manifests in cellular states, epigenetic profiles, spatial distributions and interactions with the microenvironment. Therefore, the study of cancer requires the integration of multiple heritable dimensions at the resolution of the single cell - the atomic unit of somatic evolution. In this Review, we discuss emerging analytic and experimental technologies for single-cell multi-omics that enable the capture and integration of multiple data modalities to inform the study of cancer evolution. These data show that cancer results from a complex interplay between genetic and non-genetic determinants of somatic evolution.

A.8 Proteomics

Recommended reading for Chapter 8 (listed in Section 8.9).

Aebersold and Mann (2016) — mass-spectrometric exploration of proteome structure and function; the field in one review.
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Mass-spectrometric exploration of proteome structure and function. Aebersold & Mann (2016). Nature. doi:10.1038/nature19949

Abstract

Numerous biological processes are concurrently and coordinately active in every living cell. Each of them encompasses synthetic, catalytic and regulatory functions that are, almost always, carried out by proteins organized further into higher-order structures and networks. For decades, the structures and functions of selected proteins have been studied using biochemical and biophysical methods. However, the properties and behaviour of the proteome as an integrated system have largely remained elusive. Powerful mass-spectrometry-based technologies now provide unprecedented insights into the composition, structure, function and control of the proteome, shedding light on complex biological processes and phenotypes.
Cox and Mann (2008) — MaxQuant; how a complete shotgun proteomics pipeline identifies and quantifies proteins.
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MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Cox & Mann (2008). Nature Biotechnology. doi:10.1038/nbt.1511

Abstract

Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
Elias and Gygi (2007) — the target–decoy strategy; how false discoveries are controlled in proteomics.
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Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Elias & Gygi (2007). Nature Methods. doi:10.1038/nmeth1019

Abstract

Liquid chromatography and tandem mass spectrometry (LC-MS/MS) has become the preferred method for conducting large-scale surveys of proteomes. Automated interpretation of tandem mass spectrometry (MS/MS) spectra can be problematic, however, for a variety of reasons. As most sequence search engines return results even for 'unmatchable' spectra, proteome researchers must devise ways to distinguish correct from incorrect peptide identifications. The target-decoy search strategy represents a straightforward and effective way to manage this effort. Despite the apparent simplicity of this method, some controversy surrounds its successful application. Here we clarify our preferred methodology by addressing four issues based on observed decoy hit frequencies: (i) the major assumptions made with this database search strategy are reasonable; (ii) concatenated target-decoy database searches are preferable to separate target and decoy database searches; (iii) the theoretical error associated with target-decoy false positive (FP) rate measurements can be estimated; and (iv) alternate methods for constructing decoy databases are similarly effective once certain considerations are taken into account.
Ludwig et al. (2018) — data-independent acquisition (SWATH-MS), a tutorial.
PDF · DOI · open access (CC BY 4.0)

Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Ludwig et al. (2018). Molecular Systems Biology. doi:10.15252/msb.20178126

Abstract

Many research questions in fields such as personalized medicine, drug screens or systems biology depend on obtaining consistent and quantitatively accurate proteomics data from many samples. SWATH-MS is a specific variant of data-independent acquisition (DIA) methods and is emerging as a technology that combines deep proteome coverage capabilities with quantitative consistency and accuracy. In a SWATH-MS measurement, all ionized peptides of a given sample that fall within a specified mass range are fragmented in a systematic and unbiased fashion using rather large precursor isolation windows. To analyse SWATH-MS data, a strategy based on peptide-centric scoring has been established, which typically requires prior knowledge about the chromatographic and mass spectrometric behaviour of peptides of interest in the form of spectral libraries and peptide query parameters. This tutorial provides guidelines on how to set up and plan a SWATH-MS experiment, how to perform the mass spectrometric measurement and how to analyse SWATH-MS data using peptide-centric scoring. Furthermore, concepts on how to improve SWATH-MS data acquisition, potential trade-offs of parameter settings and alternative data analysis strategies are discussed.
Yilmaz et al. (2024) — Casanovo; de novo peptide sequencing as translation with a transformer.
PDF · DOI · open access (CC BY 4.0)

Sequence-to-sequence translation from mass spectra to peptides with a transformer model. Yilmaz et al. (2024). Nature Communications. doi:10.1038/s41467-024-49731-x

Abstract

A fundamental challenge in mass spectrometry-based proteomics is the identification of the peptide that generated each acquired tandem mass spectrum. Approaches that leverage known peptide sequence databases cannot detect unexpected peptides and can be impractical or impossible to apply in some settings. Thus, the ability to assign peptide sequences to tandem mass spectra without prior information-de novo peptide sequencing-is valuable for tasks including antibody sequencing, immunopeptidomics, and metaproteomics. Although many methods have been developed to address this problem, it remains an outstanding challenge in part due to the difficulty of modeling the irregular data structure of tandem mass spectra. Here, we describe Casanovo, a machine learning model that uses a transformer neural network architecture to translate the sequence of peaks in a tandem mass spectrum into the sequence of amino acids that comprise the generating peptide. We train a Casanovo model from 30 million labeled spectra and demonstrate that the model outperforms several state-of-the-art methods on a cross-species benchmark dataset. We also develop a version of Casanovo that is fine-tuned for non-enzymatic peptides. Finally, we demonstrate that Casanovo's superior performance improves the analysis of immunopeptidomics and metaproteomics experiments and allows us to delve deeper into the dark proteome.
Uhlén et al. (2015) — the tissue-based map of the human proteome (Human Protein Atlas).
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Tissue-based map of the human proteome. Uhlén et al. (2015). Science. doi:10.1126/science.1260419

Abstract

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.

A.9 Protein structure

Recommended reading for Chapter 9 (listed in Section 9.14).

Anfinsen (1973) — why the sequence determines the structure; the basis for all structure prediction.
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Principles that Govern the Folding of Protein Chains. Anfinsen (1973). Science. doi:10.1126/science.181.4096.223
Marks et al. (2012) — protein structure prediction from sequence variation; coevolution explained.
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Protein structure prediction from sequence variation. Marks et al. (2012). Nature Biotechnology. doi:10.1038/nbt.2419

Abstract

Genomic sequences contain rich evolutionary information about functional constraints on macromolecules such as proteins. This information can be efficiently mined to detect evolutionary couplings between residues in proteins and address the long-standing challenge to compute protein three-dimensional structures from amino acid sequences. Substantial progress has recently been made on this problem owing to the explosive growth in available sequences and the application of global statistical methods. In addition to three-dimensional structure, the improved understanding of covariation may help identify functional residues involved in ligand binding, protein-complex formation and conformational changes. We expect computation of covariation patterns to complement experimental structural biology in elucidating the full spectrum of protein structures, their functional interactions and evolutionary dynamics.
Jumper et al. (2021) — AlphaFold2; the method that largely solved single-chain structure prediction.
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Highly accurate protein structure prediction with AlphaFold. Jumper et al. (2021). Nature. doi:10.1038/s41586-021-03819-2

Abstract

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1-4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6,7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence-the structure prediction component of the 'protein folding problem' 8 -has been an important open research problem for more than 50 years 9 . Despite recent progress 10-14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.
Abramson et al. (2024) — AlphaFold3; co-folding of proteins with nucleic acids, ligands and ions by diffusion.
PDF · DOI · open access (CC BY 4.0)

Accurate structure prediction of biomolecular interactions with AlphaFold 3. Abramson et al. (2024). Nature. doi:10.1038/s41586-024-07487-w

Abstract

The introduction of AlphaFold 2 1 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design 2-6 . Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein-ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein-nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody-antigen prediction accuracy compared with AlphaFold-Multimer v.2.3 7,8 . Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.
Lin et al. (2023) — ESMFold and the ESM Metagenomic Atlas; structure from a language model.
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Evolutionary-scale prediction of atomic-level protein structure with a language model. Lin et al. (2023). Science. doi:10.1126/science.ade2574

Abstract

Recent advances in machine learning have leveraged evolutionary information in multiple sequence alignments to predict protein structure. We demonstrate direct inference of full atomic-level protein structure from primary sequence using a large language model. As language models of protein sequences are scaled up to 15 billion parameters, an atomic-resolution picture of protein structure emerges in the learned representations. This results in an order-of-magnitude acceleration of high-resolution structure prediction, which enables large-scale structural characterization of metagenomic proteins. We apply this capability to construct the ESM Metagenomic Atlas by predicting structures for >617 million metagenomic protein sequences, including >225 million that are predicted with high confidence, which gives a view into the vast breadth and diversity of natural proteins.
Watson et al. (2023) — RFdiffusion; de novo design of protein structure and function with diffusion models.
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De novo design of protein structure and function with RFdiffusion. Watson et al. (2023). Nature. doi:10.1038/s41586-023-06415-8

Abstract

There has been considerable recent progress in designing new proteins using deep-learning methods 1-9 . Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models 10,11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.

A.10 Protein function

Recommended reading for Chapter 10 (listed in Section 10.9).

Ashburner et al. (2000) — the Gene Ontology; how function was turned into a computable vocabulary.
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Gene Ontology: tool for the unification of biology. Ashburner et al. (2000). Nature Genetics. doi:10.1038/75556
Whisstock and Lesk (2003) — prediction of protein function from sequence and structure; the classical review.
DOI · via SU library (SU login)

Prediction of protein function from protein sequence and structure. Whisstock & Lesk (2003). Quarterly Reviews of Biophysics. doi:10.1017/s0033583503003901

Abstract

The sequence of a genome contains the plans of the possible life of an organism, but implementation of genetic information depends on the functions of the proteins and nucleic acids that it encodes. Many individual proteins of known sequence and structure present challenges to the understanding of their function. In particular, a number of genes responsible for diseases have been identified but their specific functions are unknown. Whole-genome sequencing projects are a major source of proteins of unknown function. Annotation of a genome involves assignment of functions to gene products, in most cases on the basis of amino-acid sequence alone. 3D structure can aid the assignment of function, motivating the challenge of structural genomics projects to make structural information available for novel uncharacterized proteins. Structure-based identification of homologues often succeeds where sequence-alone-based methods fail, because in many cases evolution retains the folding pattern long after sequence similarity becomes undetectable. Nevertheless, prediction of protein function from sequence and structure is a difficult problem, because homologous proteins often have different functions. Many methods of function prediction rely on identifying similarity in sequence and/or structure between a protein of unknown function and one or more well-understood proteins. Alternative methods include inferring conservation patterns in members of a functionally uncharacterized family for which many sequences and structures are known. However, these inferences are tenuous. Such methods provide reasonable guesses at function, but are far from foolproof. It is therefore fortunate that the development of whole-organism approaches and comparative genomics permits other approaches to function prediction when the data are available. These include the use of protein-protein interaction patterns, and correlations between occurrences of related proteins in different organisms, as indicators of functional properties. Even if it is possible to ascribe a particular function to a gene product, the protein may have multiple functions. A fundamental problem is that function is in many cases an ill-defined concept. In this article we review the state of the art in function prediction and describe some of the underlying difficulties and successes.
Marcotte et al. (1999) — the Rosetta stone method; predicting function from genome context rather than homology.
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Detecting Protein Function and Protein-Protein Interactions from Genome Sequences. Marcotte et al. (1999). Science. doi:10.1126/science.285.5428.751

Abstract

A computational method is proposed for inferring protein interactions from genome sequences on the basis of the observation that some pairs of interacting proteins have homologs in another organism fused into a single protein chain. Searching sequences from many genomes revealed 6809 such putative protein-protein interactions in Escherichia coli and 45,502 in yeast. Many members of these pairs were confirmed as functionally related; computational filtering further enriches for interactions. Some proteins have links to several other proteins; these coupled links appear to represent functional interactions such as complexes or pathways. Experimentally confirmed interacting pairs are documented in a Database of Interacting Proteins.
Teufel et al. (2022) — SignalP 6.0; signal peptides of all types predicted with a protein language model.
PDF · DOI · open access (CC BY 4.0)

SignalP 6.0 predicts all five types of signal peptides using protein language models. Teufel et al. (2022). Nature Biotechnology. doi:10.1038/s41587-021-01156-3

Abstract

Signal peptides (SPs) are short amino acid sequences that control protein secretion and translocation in all living organisms. SPs can be predicted from sequence data, but existing algorithms are unable to detect all known types of SPs. We introduce SignalP 6.0, a machine learning model that detects all five SP types and is applicable to metagenomic data.
Cheng et al. (2023) — AlphaMissense; proteome-wide prediction of missense variant effects.
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Accurate proteome-wide missense variant effect prediction with AlphaMissense. Cheng et al. (2023). Science. doi:10.1126/science.adg7492

Abstract

The vast majority of missense variants observed in the human genome are of unknown clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on human and primate variant population frequency databases to predict missense variant pathogenicity. By combining structural context and evolutionary conservation, our model achieves state-of-the-art results across a wide range of genetic and experimental benchmarks, all without explicitly training on such data. The average pathogenicity score of genes is also predictive for their cell essentiality, capable of identifying short essential genes that existing statistical approaches are underpowered to detect. As a resource to the community, we provide a database of predictions for all possible human single amino acid substitutions and classify 89% of missense variants as either likely benign or likely pathogenic.
Zhou et al. (2019) — CAFA3; how function prediction is evaluated, and what the results show.
PDF · DOI · open access (CC BY 4.0)

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens. Zhou et al. (2019). Genome Biology. doi:10.1186/s13059-019-1835-8

Abstract

Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

A.11 Protein–protein interactions

Recommended reading for Chapter 11 (listed in Section 11.6).

Mering et al. (2002) — comparative assessment of large-scale interaction data; why different methods disagree.
DOI · via SU library (SU login)

Comparative assessment of large-scale data sets of protein–protein interactions. von Mering et al. (2002). Nature. doi:10.1038/nature750

Abstract

Comprehensive protein protein interaction maps promise to reveal many aspects of the complex regulatory network underlying cellular function. Recently, large-scale approaches have predicted many new protein interactions in yeast. To measure their accuracy and potential as well as to identify biases, strengths and weaknesses, we compare the methods with each other and with a reference set of previously reported protein interactions.
Luck et al. (2020) — HuRI, the reference map of the human binary interactome, and how its quality was assessed.
DOI · via SU library (SU login)

A reference map of the human binary protein interactome. Luck et al. (2020). Nature. doi:10.1038/s41586-020-2188-x

Abstract

Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype-phenotype relationships 1,2 . Here we present a human 'all-by-all' reference interactome map of human binary protein interactions, or 'HuRI'. With approximately 53,000 protein-protein interactions, HuRI has approximately four times as many such interactions as there are high-quality curated interactions from small-scale studies. The integration of HuRI with genome 3 , transcriptome 4 and proteome 5 data enables cellular function to be studied within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying the specific subcellular roles of protein-protein interactions. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases. HuRI is a systematic proteome-wide reference that links genomic variation to phenotypic outcomes.
Barabási and Oltvai (2004) — network biology; the concepts of degree, hubs, modules and motifs.
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Network biology: understanding the cell’s functional organization. Barabási & Oltvai (2004). Nature Reviews Genetics. doi:10.1038/nrg1272
Orth et al. (2010) — what flux balance analysis is, in a short primer.
DOI · via SU library (SU login)

What is flux balance analysis?. Orth et al. (2010). Nature Biotechnology. doi:10.1038/nbt.1614
Bryant et al. (2022) — predicting protein–protein interactions and complex structures with AlphaFold2 (FoldDock, pDockQ).
PDF · DOI · open access (CC BY 4.0)

Improved prediction of protein-protein interactions using AlphaFold2. Bryant et al. (2022). Nature Communications. doi:10.1038/s41467-022-28865-w

Abstract

Predicting the structure of interacting protein chains is a fundamental step towards understanding protein function. Unfortunately, no computational method can produce accurate structures of protein complexes. AlphaFold2, has shown unprecedented levels of accuracy in modelling single chain protein structures. Here, we apply AlphaFold2 for the prediction of heterodimeric protein complexes. We find that the AlphaFold2 protocol together with optimised multiple sequence alignments, generate models with acceptable quality (DockQ ≥ 0.23) for 63% of the dimers. From the predicted interfaces we create a simple function to predict the DockQ score which distinguishes acceptable from incorrect models as well as interacting from non-interacting proteins with state-of-art accuracy. We find that, using the predicted DockQ scores, we can identify 51% of all interacting pairs at 1% FPR.
Burke et al. (2023) — towards a structurally resolved human protein interaction network.
PDF · DOI · open access (CC BY 4.0)

Towards a structurally resolved human protein interaction network. Burke et al. (2023). Nature Structural & Molecular Biology. doi:10.1038/s41594-022-00910-8

Abstract

Cellular functions are governed by molecular machines that assemble through protein-protein interactions. Their atomic details are critical to studying their molecular mechanisms. However, fewer than 5% of hundreds of thousands of human protein interactions have been structurally characterized. Here we test the potential and limitations of recent progress in deep-learning methods using AlphaFold2 to predict structures for 65,484 human protein interactions. We show that experiments can orthogonally confirm higher-confidence models. We identify 3,137 high-confidence models, of which 1,371 have no homology to a known structure. We identify interface residues harboring disease mutations, suggesting potential mechanisms for pathogenic variants. Groups of interface phosphorylation sites show patterns of co-regulation across conditions, suggestive of coordinated tuning of multiple protein interactions as signaling responses. Finally, we provide examples of how the predicted binary complexes can be used to build larger assemblies helping to expand our understanding of human cell biology.

A.12 Machine learning

Recommended reading for Chapter 12 (listed in Section 12.10).

Sapoval et al. (2022) — current progress and open challenges for deep learning across the biosciences (the basis of earlier exams).
PDF · DOI · open access (CC BY 4.0)

Current progress and open challenges for applying deep learning across the biosciences. Sapoval et al. (2022). Nature Communications. doi:10.1038/s41467-022-29268-7

Abstract

Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome engineering, systems biology and data integration, and phylogenetic inference. We discuss each application area and cover the main bottlenecks of DL approaches, such as training data, problem scope, and the ability to leverage existing DL architectures in new contexts. To conclude, we provide a summary of the subject-specific and general challenges for DL across the biosciences.
Greener et al. (2021) — a guide to machine learning for biologists; how to choose and evaluate methods.
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A guide to machine learning for biologists. Greener et al. (2021). Nature Reviews Molecular Cell Biology. doi:10.1038/s41580-021-00407-0

Abstract

The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed.
LeCun et al. (2015) — deep learning, by three of its founders.
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Deep learning. LeCun et al. (2015). Nature. doi:10.1038/nature14539

Abstract

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
Krogh (2008) — what artificial neural networks are, in three pages.
DOI · via SU library (SU login)

What are artificial neural networks?. Krogh (2008). Nature Biotechnology. doi:10.1038/nbt1386

Abstract

Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for?
Walsh et al. (2021) — DOME; what a supervised machine learning study in biology should report.
DOI · via SU library (SU login)

DOME: recommendations for supervised machine learning validation in biology. Walsh et al. (2021). Nature Methods. doi:10.1038/s41592-021-01205-4
Chicco and Jurman (2020) — why the Matthews correlation coefficient is better than F1 and accuracy.
PDF · DOI · open access (CC BY 4.0)

The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. Chicco & Jurman (2020). BMC Genomics. doi:10.1186/s12864-019-6413-7

Abstract

Background To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F 1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets. Results The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset. Conclusions In this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F 1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F 1 score in evaluating binary classification tasks by all scientific communities.

A.13 Image analysis and models of cells

Recommended reading for Chapter 13 (listed in Section 13.10).

Chamier et al. (2019) — what a biologist should know about deep learning for microscopy, including its pitfalls.
DOI · open access (CC BY 4.0)

Artificial intelligence for microscopy: what you should know. von Chamier et al. (2019). Biochemical Society Transactions. doi:10.1042/bst20180391

Abstract

Artificial Intelligence based on Deep Learning (DL) is opening new horizons in biomedical research and promises to revolutionize the microscopy field. It is now transitioning from the hands of experts in computer sciences to biomedical researchers. Here, we introduce recent developments in DL applied to microscopy, in a manner accessible to non-experts. We give an overview of its concepts, capabilities and limitations, presenting applications in image segmentation, classification and restoration. We discuss how DL shows an outstanding potential to push the limits of microscopy, enhancing resolution, signal and information content in acquired data. Its pitfalls are discussed, along with the future directions expected in this field.
Stringer et al. (2020) — Cellpose; a generalist deep-learning method for cell segmentation.
DOI · via SU library (SU login)

Cellpose: a generalist algorithm for cellular segmentation. Stringer et al. (2020). Nature Methods. doi:10.1038/s41592-020-01018-x

Abstract

Many biological applications require the segmentation of cell bodies, membranes and nuclei from microscopy images. Deep learning has enabled great progress on this problem, but current methods are specialized for images that have large training datasets. Here we introduce a generalist, deep learning-based segmentation method called Cellpose, which can precisely segment cells from a wide range of image types and does not require model retraining or parameter adjustments. Cellpose was trained on a new dataset of highly varied images of cells, containing over 70,000 segmented objects. We also demonstrate a three-dimensional (3D) extension of Cellpose that reuses the two-dimensional (2D) model and does not require 3D-labeled data. To support community contributions to the training data, we developed software for manual labeling and for curation of the automated results. Periodically retraining the model on the community-contributed data will ensure that Cellpose improves constantly.
Ouyang et al. (2019) — image classification of protein localisation as a community challenge, with lessons for evaluation.
PDF · DOI · open access (CC BY 4.0)

Analysis of the Human Protein Atlas Image Classification competition. Ouyang et al. (2019). Nature Methods. doi:10.1038/s41592-019-0658-6

Abstract

Pinpointing subcellular protein localizations from microscopy images is easy to the trained eye, but challenging to automate. Based on the Human Protein Atlas image collection, we held a competition to identify deep learning solutions to solve this task. Challenges included training on highly imbalanced classes and predicting multiple labels per image. Over 3 months, 2,172 teams participated. Despite convergence on popular networks and training techniques, there was considerable variety among the solutions. Participants applied strategies for modifying neural networks and loss functions, augmenting data and using pretrained networks. The winning models far outperformed our previous effort at multi-label classification of protein localization patterns by ~20%. These models can be used as classifiers to annotate new images, feature extractors to measure pattern similarity or pretrained networks for a wide range of biological applications.
Chandrasekaran et al. (2024) — Cell Painting and image-based profiling of matched chemical and genetic perturbations (JUMP).
PDF · DOI · open access (CC BY 4.0)

Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations. Chandrasekaran et al. (2024). Nature Methods. doi:10.1038/s41592-024-02241-6

Abstract

The identification of genetic and chemical perturbations with similar impacts on cell morphology can elucidate compounds' mechanisms of action or novel regulators of genetic pathways. Research on methods for identifying such similarities has lagged due to a lack of carefully designed and well-annotated image sets of cells treated with chemical and genetic perturbations. Here we create such a Resource dataset, CPJUMP1, in which each perturbed gene's product is a known target of at least two chemical compounds in the dataset. We systematically explore the directionality of correlations among perturbations that target the same protein encoded by a given gene, and we find that identifying matches between chemical and genetic perturbations is a challenging task. Our dataset and baseline analyses provide a benchmark for evaluating methods that measure perturbation similarities and impact, and more generally, learn effective representations of cellular state from microscopy images. Such advancements would accelerate the applications of image-based profiling of cellular states, such as uncovering drug mode of action or probing functional genomics.
Bunne et al. (2024) — the AI virtual cell; what it is meant to be and how it could be built.
PDF · DOI · open access (CC BY 4.0)

How to build the virtual cell with artificial intelligence: Priorities and opportunities. Bunne et al. (2024). Cell. doi:10.1016/j.cell.2024.11.015

Abstract

Cells are essential to understanding health and disease, yet traditional models fall short of modeling and simulating their function and behavior. Advances in AI and omics offer groundbreaking opportunities to create an AI virtual cell (AIVC), a multi-scale, multi-modal large-neural-network-based model that can represent and simulate the behavior of molecules, cells, and tissues across diverse states. This Perspective provides a vision on their design and how collaborative efforts to build AIVCs will transform biological research by allowing high-fidelity simulations, accelerating discoveries, and guiding experimental studies, offering new opportunities for understanding cellular functions and fostering interdisciplinary collaborations in open science.
Ahlmann-Eltze et al. (2025) — a critical test; deep-learning models of perturbation effects against simple baselines.
PDF · DOI · open access (CC BY 4.0)

Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines. Ahlmann-Eltze et al. (2025). Nature Methods. doi:10.1038/s41592-025-02772-6

Abstract

Recent research in deep-learning-based foundation models promises to learn representations of single-cell data that enable prediction of the effects of genetic perturbations. Here we compared five foundation models and two other deep learning models against deliberately simple baselines for predicting transcriptome changes after single or double perturbations. None outperformed the baselines, which highlights the importance of critical benchmarking in directing and evaluating method development.
Thornburg et al. (2026) — a mechanistic, spatial model of the complete cell cycle of a minimal cell.
DOI · open access (CC BY 4.0)

Bringing the genetically minimal cell to life on a computer in 4D. Thornburg et al. (2026). Cell. doi:10.1016/j.cell.2026.02.009

Abstract

We present a whole-cell spatial and kinetic model for the ∼100 min cell cycle of the genetically minimal bacterium JCVI-syn3A. We simulate the complete cell cycle in 4D (space and time), including all genetic information processes, metabolic networks, growth, and cell division. By integrating hybrid computational methods, we model the dynamics of morphological transformations. Growth is driven by insertion of lipids and membrane proteins and constrained by fluorescence imaging data. Chromosome replication and segregation are controlled by the essential structural maintenance of chromosome proteins, analogous to condensin (SMC) and topoisomerase proteins in Brownian dynamics simulations, with replication rates responding to deoxyribonucleotide triphosphate (dNTP) pools from metabolism. The model captures the origin-to-terminus ratio measured in our DNA sequencing and recovers other experimental measurements, such as doubling time, mRNA half-lives, protein distributions, and ribosome counts. Because of stochasticity, each replicate cell is unique. We predict not only the average behavior of partitioning to daughter cells but also the heterogeneity among them.

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