Annotation inconsistencies beyond sequence similarity-based function prediction - phylogeny and genome structure

Analysis of Antimicrobial Resistance in Bacterial Pathogens Recovered from Food and Human Sources: Insights from 639,087 Bacterial Whole-Genome Sequences in the NCBI Pathogen Detection Database

Understanding the role of foods in the emergence and spread of antimicrobial resistance necessitates the initial documentation of antibiotic resistance genes within bacterial species found in foods. Here, the NCBI Pathogen Detection database was used to query antimicrobial resistance gene prevalence in foodborne and human clinical bacterial isolates. Of the 1,843,630 sequence entries, 639,087 (34.7%) were assigned to foodborne or human clinical sources with 147,788 (23.14%) from food and 427,614 (76.88%) from humans. The majority of foodborne isolates were either Salmonella (47.88%), Campylobacter (23.03%), Escherichia (11.79%), or Listeria (11.3%), and the remaining 6% belonged to 20 other genera. Most foodborne isolates were from meat/poultry (95,251 or 64.45%), followed by multi-product mixed food sources (29,892 or 20.23%) and fish/seafood (6503 or 4.4%); however, the most prominent isolation source varied depending on the genus/species. Resistance gene carriage also varied depending on isolation source and genus/species. Of note, Klebsiella pneumoniae and Enterobacter spp. carried larger proportions of the quinolone resistance gene qnrS and some clinically relevant beta-lactam resistance genes in comparison to Salmonella and Escherichia coli. The prevalence of mec in S. aureus did not significantly differ between meat/poultry and multi-product sources relative to clinical sources, whereas this resistance was rare in isolates from dairy sources. The proportion of biocide resistance in Bacillus and Escherichia was significantly higher in clinical isolates compared to many foodborne sources but significantly lower in clinical Listeria compared to foodborne Listeria. This work exposes the gaps in current publicly available sequence data repositories, which are largely composed of clinical isolates and are biased towards specific highly abundant pathogenic species. We also highlight the importance of requiring and curating metadata on sequence submission to not only ensure correct information and data interpretation but also foster efficient analysis, sharing, and collaboration. To effectively monitor resistance carriage in food production, additional work on sequencing and characterizing AMR carriage in common commensal foodborne bacteria is critical.

The impact of transitive annotation on the training of taxonomic classifiers

Introduction

A common task in the analysis of microbial communities involves assigning taxonomic labels to the sequences derived from organisms found in the communities. Frequently, such labels are assigned using machine learning algorithms that are trained to recognize individual taxonomic groups based on training data sets that comprise sequences with known taxonomic labels. Ideally, the training data should rely on labels that are experimentally verified-formal taxonomic labels require knowledge of physical and biochemical properties of organisms that cannot be directly inferred from sequence alone. However, the labels associated with sequences in biological databases are most commonly computational predictions which themselves may rely on computationally-generated data-a process commonly referred to as "transitive annotation."

Methods

In this manuscript we explore the implications of training a machine learning classifier (the Ribosomal Database Project's Bayesian classifier in our case) on data that itself has been computationally generated. We generate new training examples based on 16S rRNA data from a metagenomic experiment, and evaluate the extent to which the taxonomic labels predicted by the classifier change after re-training.

Results

We demonstrate that even a few computationally-generated training data points can significantly skew the output of the classifier to the point where entire regions of the taxonomic space can be disturbed.

Discussion and conclusions

We conclude with a discussion of key factors that affect the resilience of classifiers to transitively-annotated training data, and propose best practices to avoid the artifacts described in our paper.

Propagation, detection and correction of errors using the sequence database network

Nucleotide and protein sequences stored in public databases are the cornerstone of many bioinformatics analyses. The records containing these sequences are prone to a wide range of errors, including incorrect functional annotation, sequence contamination and taxonomic misclassification. One source of information that can help to detect errors are the strong interdependency between records. Novel sequences in one database draw their annotations from existing records, may generate new records in multiple other locations and will have varying degrees of similarity with existing records across a range of attributes. A network perspective of these relationships between sequence records, within and across databases, offers new opportunities to detect-or even correct-erroneous entries and more broadly to make inferences about record quality. Here, we describe this novel perspective of sequence database records as a rich network, which we call the sequence database network, and illustrate the opportunities this perspective offers for quantification of database quality and detection of spurious entries. We provide an overview of the relevant databases and describe how the interdependencies between sequence records across these databases can be exploited by network analyses. We review the process of sequence annotation and provide a classification of sources of error, highlighting propagation as a major source. We illustrate the value of a network perspective through three case studies that use network analysis to detect errors, and explore the quality and quantity of critical relationships that would inform such network analyses. This systematic description of a network perspective of sequence database records provides a novel direction to combat the proliferation of errors within these critical bioinformatics resources.

Innovative Hybrid-Alignment Annotation Method for Bioinformatics Identification and Functional Verification of a Novel Nitric Oxide Synthase in Trichomonas vaginalis

Simple Summary

Both the annotation and identification of genes in pathogenic parasites remain challenging. As a survival factor, nitric oxide (NO) has been proven to be synthesized in Trichomonas vaginalis (TV). However, nitric oxide synthase (NOS) has not yet been annotated in the TV genome. By aligning whole coding sequences of TV against a thousand sequences of known proteins from other organisms via the Smith–Waterman and Needleman–Wunsch algorithms, we developed a witness-to-suspect strategy to identify incorrectly annotated genes in TV. A novel NOS of TV (TV NOS) with a high witness-to-suspect ratio, which was originally annotated as a hydrogenase in the NCBI database, was successfully identified. We then performed in silico modeling of the protein structure and the molecular docking of all cofactors (NADPH, tetrahydrobiopterin (BH4), heme and flavin adenine dinucleotide (FAD)), cloned the gene, expressed and purified the protein, and ultimately performed mass spectrometry analysis and enzymatic activity assays. We clearly showed that although the predicted structure of TV NOS is not similar to that of NOS proteins of other species, all cofactor-binding motifs can interact with their ligands with high affinities. Most importantly, the purified protein is a functional NOS, as it has a high enzymatic activity for generating NO in vitro. This study provides an innovative approach to identify incorrectly annotated genes.

Abstract

Both the annotation and identification of genes in pathogenic parasites are still challenging. Although, as a survival factor, nitric oxide (NO) has been proven to be synthesized in Trichomonas vaginalis (TV), nitric oxide synthase (NOS) has not yet been annotated in the TV genome. We developed a witness-to-suspect strategy to identify incorrectly annotated genes in TV via the Smith–Waterman and Needleman–Wunsch algorithms through in-depth and repeated alignment of whole coding sequences of TV against thousands of sequences of known proteins from other organisms. A novel NOS of TV (TV NOS), which was annotated as hydrogenase in the NCBI database, was successfully identified; this TV NOS had a high witness-to-suspect ratio and contained all the NOS cofactor-binding motifs (NADPH, tetrahydrobiopterin (BH4), heme and flavin adenine dinucleotide (FAD) motifs). To confirm this identification, we performed in silico modeling of the protein structure and cofactor docking, cloned the gene, expressed and purified the protein, performed mass spectrometry analysis, and ultimately performed an assay to measure enzymatic activity. Our data showed that although the predicted structure of the TV NOS protein was not similar to the structure of NOSs of other species, all cofactor-binding motifs could interact with their ligands with high affinities. We clearly showed that the purified protein had high enzymatic activity for generating NO in vitro. This study provides an innovative approach to identify incorrectly annotated genes in TV and highlights a novel NOS that might serve as a virulence factor of TV.

Functional characterization of prokaryotic dark matter: the road so far and what lies ahead

Eight-hundred thousand to one trillion prokaryotic species may inhabit our planet. Yet, fewer than two-hundred thousand prokaryotic species have been described. This uncharted fraction of microbial diversity, and its undisclosed coding potential, is known as the "microbial dark matter" (MDM). Next-generation sequencing has allowed to collect a massive amount of genome sequence data, leading to unprecedented advances in the field of genomics. Still, harnessing new functional information from the genomes of uncultured prokaryotes is often limited by standard classification methods. These methods often rely on sequence similarity searches against reference genomes from cultured species. This hinders the discovery of unique genetic elements that are missing from the cultivated realm. It also contributes to the accumulation of prokaryotic gene products of unknown function among public sequence data repositories, highlighting the need for new approaches for sequencing data analysis and classification. Increasing evidence indicates that these proteins of unknown function might be a treasure trove of biotechnological potential. Here, we outline the challenges, opportunities, and the potential hidden within the functional dark matter (FDM) of prokaryotes. We also discuss the pitfalls surrounding molecular and computational approaches currently used to probe these uncharted waters, and discuss future opportunities for research and applications.

Turtle ants harbor metabolically versatile microbiomes with conserved functions across development and phylogeny

Gut bacterial symbionts can support animal nutrition by facilitating digestion and providing valuable metabolites. However, changes in symbiotic roles between immature and adult stages are not well documented, especially in ants. Here, we explored the metabolic capabilities of microbiomes sampled from herbivorous turtle ant (Cephalotes sp.) larvae and adult workers through (meta)genomic screening and in vitro metabolic assays. We reveal that larval guts harbor bacterial symbionts with impressive metabolic capabilities, including catabolism of plant and fungal recalcitrant dietary fibers and energy-generating fermentation. Additionally, several members of the specialized adult gut microbiome, sampled downstream of an anatomical barrier that dams large food particles, show a conserved potential to depolymerize many dietary fibers. Symbionts from both life stages have the genomic capacity to recycle nitrogen and synthesize amino acids and B-vitamins. With help of their gut symbionts, including several bacteria likely acquired from the environment, turtle ant larvae may aid colony digestion and contribute to colony-wide nitrogen, B-vitamin and energy budgets. In addition, the conserved nature of the digestive capacities among adult-associated symbionts suggests that nutritional ecology of turtle ant colonies has long been shaped by specialized, behaviorally-transferred gut bacteria with over 45 million years of residency.

Identification of human gene research articles with wrongly identified nucleotide sequences

Nucleotide sequence reagents underpin molecular techniques that have been applied across hundreds of thousands of publications. We have previously reported wrongly identified nucleotide sequence reagents in human research publications and described a semi-automated screening tool Seek & Blastn to fact-check their claimed status. We applied Seek & Blastn to screen >11,700 publications across five literature corpora, including all original publications in Gene from 2007 to 2018 and all original open-access publications in Oncology Reports from 2014 to 2018. After manually checking Seek & Blastn outputs for >3,400 human research articles, we identified 712 articles across 78 journals that described at least one wrongly identified nucleotide sequence. Verifying the claimed identities of >13,700 sequences highlighted 1,535 wrongly identified sequences, most of which were claimed targeting reagents for the analysis of 365 human protein-coding genes and 120 non-coding RNAs. The 712 problematic articles have received >17,000 citations, including citations by human clinical trials. Given our estimate that approximately one-quarter of problematic articles may misinform the future development of human therapies, urgent measures are required to address unreliable gene research articles.

SeqWho: reliable, rapid determination of sequence file identity using k-mer frequencies in Random Forest classifiers

MOTIVATION

With the vast improvements in sequencing technologies and increased number of protocols, sequencing is being used to answer complex biological problems. Subsequently, analysis pipelines have become more time consuming and complicated, usually requiring highly extensive prevalidation steps. Here, we present SeqWho, a program designed to assess heuristically the quality of sequencing files and reliably classify the organism and protocol type by using Random Forest classifiers trained on biases native in k-mer frequencies and repeat sequence identities.

RESULTS

Using one of our primary models, we show that our method accurately and rapidly classifies human and mouse sequences from nine different sequencing libraries by species, library and both together, 98.32%, 97.86% and 96.38% of the time, respectively. Ultimately, we demonstrate that SeqWho is a powerful method for reliably validating the quality and identity of the sequencing files used in any pipeline.

AVAILABILITY AND IMPLEMENTATION

https://github.com/DaehwanKimLab/seqwho.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Homeolog expression quantification methods for allopolyploids

Genome duplication with hybridization, or allopolyploidization, occurs in animals, fungi and plants, and is especially common in crop plants. There is an increasing interest in the study of allopolyploids because of advances in polyploid genome assembly; however, the high level of sequence similarity in duplicated gene copies (homeologs) poses many challenges. Here we compared standard RNA-seq expression quantification approaches used currently for diploid species against subgenome-classification approaches which maps reads to each subgenome separately. We examined mapping error using our previous and new RNA-seq data in which a subgenome is experimentally added (synthetic allotetraploid Arabidopsis kamchatica) or reduced (allohexaploid wheat Triticum aestivum versus extracted allotetraploid) as ground truth. The error rates in the two species were very similar. The standard approaches showed higher error rates (>10% using pseudo-alignment with Kallisto) while subgenome-classification approaches showed much lower error rates (<1% using EAGLE-RC, <2% using HomeoRoq). Although downstream analysis may partly mitigate mapping errors, the difference in methods was substantial in hexaploid wheat, where Kallisto appeared to have systematic differences relative to other methods. Only approximately half of the differentially expressed homeologs detected using Kallisto overlapped with those by any other method in wheat. In general, disagreement in low-expression genes was responsible for most of the discordance between methods, which is consistent with known biases in Kallisto. We also observed that there exist uncertainties in genome sequences and annotation which can affect each method differently. Overall, subgenome-classification approaches tend to perform better than standard approaches with EAGLE-RC having the highest precision.

Open Issues for Protein Function Assignment in Haloferax volcanii and Other Halophilic Archaea

Background: Annotation ambiguities and annotation errors are a general challenge in genomics. While a reliable protein function assignment can be obtained by experimental characterization, this is expensive and time-consuming, and the number of such Gold Standard Proteins (GSP) with experimental support remains very low compared to proteins annotated by sequence homology, usually through automated pipelines. Even a GSP may give a misleading assignment when used as a reference: the homolog may be close enough to support isofunctionality, but the substrate of the GSP is absent from the species being annotated. In such cases, the enzymes cannot be isofunctional. Here, we examined a variety of such issues in halophilic archaea (class Halobacteria), with a strong focus on the model haloarchaeon Haloferax volcanii. Results: Annotated proteins of Hfx. volcanii were identified for which public databases tend to assign a function that is probably incorrect. In some cases, an alternative, probably correct, function can be predicted or inferred from the available evidence, but this has not been adopted by public databases because experimental validation is lacking. In other cases, a probably invalid specific function is predicted by homology, and while there is evidence that this assigned function is unlikely, the true function remains elusive. We listed 50 of those cases, each with detailed background information, so that a conclusion about the most likely biological function can be drawn. For reasons of brevity and comprehension, only the key aspects are listed in the main text, with detailed information being provided in a corresponding section of the Supplementary Materials. Conclusions: Compiling, describing and summarizing these open annotation issues and functional predictions will benefit the scientific community in the general effort to improve the evaluation of protein function assignments and more thoroughly detail them. By highlighting the gaps and likely annotation errors currently in the databases, we hope this study will provide a framework for experimentalists to systematically confirm (or disprove) our function predictions or to uncover yet more unexpected functions.

Mantis: flexible and consensus-driven genome annotation

Background

The rapid development of the (meta-)omics fields has produced an unprecedented amount of high-resolution and high-fidelity data. Through the use of these datasets we can infer the role of previously functionally unannotated proteins from single organisms and consortia. In this context, protein function annotation can be described as the identification of regions of interest (i.e., domains) in protein sequences and the assignment of biological functions. Despite the existence of numerous tools, challenges remain in terms of speed, flexibility, and reproducibility. In the big data era, it is also increasingly important to cease limiting our findings to a single reference, coalescing knowledge from different data sources, and thus overcoming some limitations in overly relying on computationally generated data from single sources.

Results

We implemented a protein annotation tool, Mantis, which uses database identifiers intersection and text mining to integrate knowledge from multiple reference data sources into a single consensus-driven output. Mantis is flexible, allowing for the customization of reference data and execution parameters, and is reproducible across different research goals and user environments. We implemented a depth-first search algorithm for domain-specific annotation, which significantly improved annotation performance compared to sequence-wide annotation. The parallelized implementation of Mantis results in short runtimes while also outputting high coverage and high-quality protein function annotations.

Conclusions

Mantis is a protein function annotation tool that produces high-quality consensus-driven protein annotations. It is easy to set up, customize, and use, scaling from single genomes to large metagenomes. Mantis is available under the MIT license at https://github.com/PedroMTQ/mantis.

Accelerating biological insight for understudied genes

The rapid expansion of genome sequence data is increasing the discovery of protein-coding genes across all domains of life. Annotating these genes with reliable functional information is necessary to understand evolution, to define the full biochemical space accessed by nature, and to identify target genes for biotechnology improvements. The vast majority of proteins are annotated based on sequence conservation with no specific biological, biochemical, genetic, or cellular function identified. Recent technical advances throughout the biological sciences enable experimental research on these understudied protein-coding genes in a broader collection of species. However, scientists have incentives and biases to continue focusing on well documented genes within their preferred model organism. This perspective suggests a research model that seeks to break historic silos of research bias by enabling interdisciplinary teams to accelerate biological functional annotation. We propose an initiative to develop coordinated projects of collaborating evolutionary biologists, cell biologists, geneticists, and biochemists that will focus on subsets of target genes in multiple model organisms. Concurrent analysis in multiple organisms takes advantage of evolutionary divergence and selection, which causes individual species to be better suited as experimental models for specific genes. Most importantly, multisystem approaches would encourage transdisciplinary critical thinking and hypothesis testing that is inherently slow in current biological research.

Leveraging Curation Among Escherichia coli Pathway/Genome Databases Using Ortholog-Based Annotation Propagation

Updating genome databases to reflect newly published molecular findings for an organism was hard enough when only a single strain of a given organism had been sequenced. With multiple sequenced strains now available for many organisms, the challenge has grown significantly because of the still-limited resources available for the manual curation that corrects errors and captures new knowledge. We have developed a method to automatically propagate multiple types of curated knowledge from genes and proteins in one genome database to their orthologs in uncurated databases for related strains, imposing several quality-control filters to reduce the chances of introducing errors. We have applied this method to propagate information from the highly curated EcoCyc database for Escherichia coli K-12 to databases for 480 other Escherichia coli strains in the BioCyc database collection. The increase in value and utility of the target databases after propagation is considerable. Target databases received updates for an average of 2,535 proteins each. In addition to widespread addition and regularization of gene and protein names, 97% of the target databases were improved by the addition of at least 200 new protein complexes, at least 800 new or updated reaction assignments, and at least 2,400 sets of GO annotations.

Bacterial riboproteogenomics: the era of N-terminal proteoform existence revealed

With the rapid increase in the number of sequenced prokaryotic genomes, relying on automated gene annotation became a necessity. Multiple lines of evidence, however, suggest that current bacterial genome annotations may contain inconsistencies and are incomplete, even for so-called well-annotated genomes. We here discuss underexplored sources of protein diversity and new methodologies for high-throughput genome reannotation. The expression of multiple molecular forms of proteins (proteoforms) from a single gene, particularly driven by alternative translation initiation, is gaining interest as a prominent contributor to bacterial protein diversity. In consequence, riboproteogenomic pipelines were proposed to comprehensively capture proteoform expression in prokaryotes by the complementary use of (positional) proteomics and the direct readout of translated genomic regions using ribosome profiling. To complement these discoveries, tailored strategies are required for the functional characterization of newly discovered bacterial proteoforms.

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

GP4: an integrated Gram-Positive Protein Prediction Pipeline for subcellular localization mimicking bacterial sorting

Subcellular localization is a critical aspect of protein function and the potential application of proteins either as drugs or drug targets, or in industrial and domestic applications. However, the experimental determination of protein localization is time consuming and expensive. Therefore, various localization predictors have been developed for particular groups of species. Intriguingly, despite their major representation amongst biotechnological cell factories and pathogens, a meta-predictor based on sorting signals and specific for Gram-positive bacteria was still lacking. Here we present GP⁴, a protein subcellular localization meta-predictor mainly for Firmicutes, but also Actinobacteria, based on the combination of multiple tools, each specific for different sorting signals and compartments. Novelty elements include improved cell-wall protein prediction, including differentiation of the type of interaction, prediction of non-canonical secretion pathway target proteins, separate prediction of lipoproteins and better user experience in terms of parsability and interpretability of the results. GP⁴ aims at mimicking protein sorting as it would happen in a bacterial cell. As GP⁴ is not homology based, it has a broad applicability and does not depend on annotated databases with homologous proteins. Non-canonical usage may include little studied or novel species, synthetic and engineered organisms, and even re-use of the prediction data to develop custom prediction algorithms. Our benchmark analysis highlights the improved performance of GP⁴ compared to other widely used subcellular protein localization predictors. A webserver running GP⁴ is available at http://gp4.hpc.rug.nl/

ARGDIT: a validation and integration toolkit for Antimicrobial Resistance Gene Databases

MOTIVATION

Antimicrobial resistance is currently one of the main challenges in public health due to the excessive use of antimicrobials in medical treatments and agriculture. The advancements in high-throughput next-generation sequencing and development of bioinformatics tools allow simultaneous detection and identification of antimicrobial resistance genes (ARGs) from clinical, food and environment samples, to monitor the prevalence and track the dissemination of these ARGs. Such analyses are however reliant on a comprehensive database of ARGs with accurate sequence content and annotation. Most of the current ARG databases are therefore manually curated, but this is a time-consuming process and the resulting curation errors could be hard to detect. Several secondary ARG databases consolidate contents from different source ARG databases, and hence modifications in the primary databases might not be propagated and updated promptly in the secondary ARG databases.

RESULTS

To address these problems, a validation and integration toolkit called ARGDIT was developed to validate ARG database fidelity, and merge multiple primary ARG databases into a single consolidated secondary ARG database with optional automated sequence re-annotation. Experimental results demonstrated the effectiveness of this toolkit in identifying errors such as sequence annotation typos in current ARG databases and generating an integrated non-redundant ARG database with structured annotation. A toolkit-oriented workflow is also proposed to minimize the efforts in validating, curating and merging multiple ARG protein or coding sequence databases. Database developers therefore benefit from faster update cycles and lower costs for database maintenance, while ARG pipeline users can easily evaluate the reference ARG database quality.

AVAILABILITY AND IMPLEMENTATION

ARGDIT is available at https://github.com/phglab/ARGDIT.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Misannotations of the genes encoding sugar N-formyltransferases

Tens of thousands of bacterial genome sequences are now known due to the development of rapid and inexpensive sequencing technologies. An important key in utilizing these vast amounts of data in a biologically meaningful way is to infer the function of the proteins encoded in the genomes via bioinformatics techniques. Whereas these approaches are absolutely critical to the annotation of gene function, there are still issues of misidentifications, which must be experimentally corrected. For example, many of the bacterial DNA sequences encoding sugar N-formyltransferases have been annotated as l-methionyl-tRNA transferases in the databases. These mistakes may be due in part to the fact that until recently the structures and functions of these enzymes were not well known. Herein we describe the misannotation of two genes, WP_088211966.1 and WP_096244125.1, from Shewanella spp. and Pseudomonas congelans, respectively. Although the proteins encoded by these genes were originally suggested to function as l-methionyl-tRNA transferases, we demonstrate that they actually catalyze the conversion of dTDP-4-amino-4,6-dideoxy-d-glucose to dTDP-4-formamido-4,6-dideoxy-d-glucose utilizing N10 -formyltetrahydrofolate as the carbon source. For this analysis, the genes encoding these enzymes were cloned and the corresponding proteins purified. X-ray structures of the two proteins were determined to high resolution and kinetic analyses were conducted. Both enzymes display classical Michaelis-Menten kinetics and adopt the characteristic three-dimensional structural fold previously observed for other sugar N-formyltransferases. The results presented herein will aid in the future annotation of these fascinating enzymes.

Tandem repeats lead to sequence assembly errors and impose multi-level challenges for genome and protein databases

The widespread occurrence of repetitive stretches of DNA in genomes of organisms across the tree of life imposes fundamental challenges for sequencing, genome assembly, and automated annotation of genes and proteins. This multi-level problem can lead to errors in genome and protein databases that are often not recognized or acknowledged. As a consequence, end users working with sequences with repetitive regions are faced with 'ready-to-use' deposited data whose trustworthiness is difficult to determine, let alone to quantify. Here, we provide a review of the problems associated with tandem repeat sequences that originate from different stages during the sequencing-assembly-annotation-deposition workflow, and that may proliferate in public database repositories affecting all downstream analyses. As a case study, we provide examples of the Atlantic cod genome, whose sequencing and assembly were hindered by a particularly high prevalence of tandem repeats. We complement this case study with examples from other species, where mis-annotations and sequencing errors have propagated into protein databases. With this review, we aim to raise the awareness level within the community of database users, and alert scientists working in the underlying workflow of database creation that the data they omit or improperly assemble may well contain important biological information valuable to others.

PTBP1 mRNA isoforms and regulation of their translation

Polypyrimidine tract-binding proteins (PTBPs) are RNA binding proteins that regulate a number of posttranscriptional events. Human PTBP1 transits between the nucleus and cytoplasm and is thought to regulate RNA processes in both. However, information about PTBP1 mRNA isoforms and regulation of PTPB1 expression remains incomplete. Here we mapped the major PTBP1 mRNA isoforms in HEK293T cells and identified alternative 5' and 3' untranslated regions (5'-UTRs, 3'-UTRs), as well as alternative splicing patterns in the protein coding region. We also assessed how the observed PTBP1 mRNA isoforms contribute to PTBP1 expression in different phases of the cell cycle. Previously, PTBP1 mRNAs were shown to crosslink to eukaryotic translation initiation factor 3 (eIF3). We find that eIF3 binds differently to each PTBP1 mRNA isoform in a cell cycle dependent manner. We also observe a strong correlation between eIF3 binding to PTBP1 mRNAs and repression of PTBP1 levels during the S phase of the cell cycle. Our results provide evidence of translational regulation of PTBP1 protein levels during the cell cycle, which may affect downstream regulation of alternative splicing and translation mediated by PTBP1 protein isoforms.

Update on the epidemiological typing methods for Acinetobacter baumannii

The outstanding ability of Acinetobacter baumannii to cause outbreaks and acquire multidrug resistance motivated the development of a plethora of typing techniques, which can help infection preventionists and hospital epidemiologists to more efficiently implement intervention controls. Nowadays, the world is witnessing a gradual transition from traditional typing methodology to whole genome sequencing-based approaches. Such approaches are opening new prospects and applications never achieved by existing typing methods. Herein, we provide the reader with an updated review on A. baumannii typing methods recapping the added value of well-established techniques previously applied for A. baumannii and detailing new ones (as clustered regularly interspaced short palindromic repeats-based typing) with a special focus on whole genome sequencing.

BDBM 1.0: A Desktop Application for Efficient Retrieval and Processing of High-Quality Sequence Data and Application to the Identification of the Putative Coffea S-Locus

Nowadays, bioinformatics is one of the most important areas in modern biology and the creation of high-quality scientific software supporting this recent research area is one of the core activities of many researchers. In this context, high-quality sequence datasets are needed to perform inferences on the evolution of species, genes, and gene families, or to get evidence for adaptive amino acid evolution, among others. Nevertheless, sequence data are very often spread over several databases, many useful genomes and transcriptomes are non-annotated, the available annotation is not for the desired coding sequence isoform, and/or is unlikely to be accurate. Moreover, although the FASTA text-based format is quite simple and usable by most software applications, there are a number of issues that may be critical depending on the software used to analyse such files. Therefore, researchers without training in informatics often use a fraction of all available data. The above issues can be addressed using already available software applications, but there is no easy-to-use single piece of software that allows performing all these tasks within the same graphical interface, such as the one here presented, named BDBM (Blast DataBase Manager). BDBM can be used to efficiently get gene sequences from annotated and non-annotated genomes and transcriptomes. Moreover, it can be used to look for alternatives to existing annotations and to easily create reliable custom databases. Such databases are essential to prepare high-quality datasets. The analyses that we have performed on the Coffea canephora genome using BDBM aimed at the identification of the S-locus region (that harbours the genes involved in gametophytic self-incompatibility) led to the conclusion that there are two likely regions, one on chromosome 2 (around region 6600000-6650000), and another on chromosome 5 (around 15830000-15930000). Such findings are discussed in the context of the Rubiaceae gametophytic self-incompatibility evolution.

No wisdom in the crowd: genome annotation in the era of big data - current status and future prospects

Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains essential in biological research and reflects the nature of biology itself. Annotation systems, rather than being repositories of facts, should be tools that support multiple modes of inference. By combining deduction, induction and abduction, investigators can generate hypotheses when accurate knowledge is extracted from model databases. A key stance is to depart from 'the sequence tells the structure tells the function' fallacy, placing function first. We illustrate our approach with examples of critical or unexpected pathways, using MicroScope to demonstrate how tools can be implemented following the principles we advocate. We end with a challenge to the reader.

Strategies to improve usability and preserve accuracy in biological sequence databases

Biology is increasingly dependent on large-scale analysis, such as proteomics, creating a requirement for efficient bioinformatics. Bioinformatic predictions of biological functions rely upon correctly annotated database sequences, and the presence of inaccurately annotated or otherwise poorly described sequences introduces noise and bias to biological analyses. Accurate annotations are, for example, pivotal for correct identification of polypeptide fragments. However, standards for how sequence databases are organized and presented are currently insufficient. Here, we propose five strategies to address fundamental issues in the annotation of sequence databases: (i) to clearly separate experimentally verified and unverified sequence entries; (ii) to enable a system for tracing the origins of annotations; (iii) to separate entries with high-quality, informative annotation from less useful ones; (iv) to integrate automated quality-control software whenever such tools exist; and (v) to facilitate postsubmission editing of annotations and metadata associated with sequences. We believe that implementation of these strategies, for example as requirements for publication of database papers, would enable biology to better take advantage of large-scale data.

Comparative Genomic Analysis of the Human Gut Microbiome Reveals a Broad Distribution of Metabolic Pathways for the Degradation of Host-Synthetized Mucin Glycans and Utilization of Mucin-Derived Monosaccharides

The colonic mucus layer is a dynamic and complex structure formed by secreted and transmembrane mucins, which are high-molecular-weight and heavily glycosylated proteins. Colonic mucus consists of a loose outer layer and a dense epithelium-attached layer. The outer layer is inhabited by various representatives of the human gut microbiota (HGM). Glycans of the colonic mucus can be used by the HGM as a source of carbon and energy when dietary fibers are not sufficiently available. Both commensals and pathogens can utilize mucin glycans. Commensals are mostly involved in the cleavage of glycans, while pathogens mostly utilize monosaccharides released by commensals. This HGM-derived degradation of the mucus layer increases pathogen susceptibility and causes many other health disorders. Here, we analyzed 397 individual HGM genomes to identify pathways for the cleavage of host-synthetized mucin glycans to monosaccharides as well as for the catabolism of the derived monosaccharides. Our key results are as follows: (i) Genes for the cleavage of mucin glycans were found in 86% of the analyzed genomes, which significantly higher than a previous estimation. (ii) Genes for the catabolism of derived monosaccharides were found in 89% of the analyzed genomes. (iii) Comparative genomic analysis identified four alternative forms of the monosaccharide-catabolizing enzymes and four alternative forms of monosaccharide transporters. (iv) Eighty-five percent of the analyzed genomes may be involved in potential feeding pathways for the monosaccharides derived from cleaved mucin glycans. (v) The analyzed genomes demonstrated different abilities to degrade known mucin glycans. Generally, the ability to degrade at least one type of mucin glycan was predicted for 81% of the analyzed genomes. (vi) Eighty-two percent of the analyzed genomes can form mutualistic pairs that are able to degrade mucin glycans and are not degradable by any of the paired organisms alone. Taken together, these findings provide further insight into the inter-microbial communications of the HGM as well as into host-HGM interactions.

Uncovering missing pieces: duplication and deletion history of arrestins in deuterostomes

BACKGROUND

The cytosolic arrestin proteins mediate desensitization of activated G protein-coupled receptors (GPCRs) via competition with G proteins for the active phosphorylated receptors. Arrestins in active, including receptor-bound, conformation are also transducers of signaling. Therefore, this protein family is an attractive therapeutic target. The signaling outcome is believed to be a result of structural and sequence-dependent interactions of arrestins with GPCRs and other protein partners. Here we elucidated the detailed evolution of arrestins in deuterostomes.

RESULTS

Identity and number of arrestin paralogs were determined searching deuterostome genomes and gene expression data. In contrast to standard gene prediction methods, our strategy first detects exons situated on different scaffolds and then solves the problem of assigning them to the correct gene. This increases both the completeness and the accuracy of the annotation in comparison to conventional database search strategies applied by the community. The employed strategy enabled us to map in detail the duplication- and deletion history of arrestin paralogs including tandem duplications, pseudogenizations and the formation of retrogenes. The two rounds of whole genome duplications in the vertebrate stem lineage gave rise to four arrestin paralogs. Surprisingly, visual arrestin ARR3 was lost in the mammalian clades Afrotheria and Xenarthra. Duplications in specific clades, on the other hand, must have given rise to new paralogs that show signatures of diversification in functional elements important for receptor binding and phosphate sensing.

CONCLUSION

The current study traces the functional evolution of deuterostome arrestins in unprecedented detail. Based on a precise re-annotation of the exon-intron structure at nucleotide resolution, we infer the gain and loss of paralogs and patterns of conservation, co-variation and selection.

Automated detection of records in biological sequence databases that are inconsistent with the literature

We investigate and analyse the data quality of nucleotide sequence databases with the objective of automatic detection of data anomalies and suspicious records. Specifically, we demonstrate that the published literature associated with each data record can be used to automatically evaluate its quality, by cross-checking the consistency of the key content of the database record with the referenced publications. Focusing on GenBank, we describe a set of quality indicators based on the relevance paradigm of information retrieval (IR). Then, we use these quality indicators to train an anomaly detection algorithm to classify records as "confident" or "suspicious". Our experiments on the PubMed Central collection show assessing the coherence between the literature and database records, through our algorithms, is an effective mechanism for assisting curators to perform data cleansing. Although fewer than 0.25% of the records in our data set are known to be faulty, we would expect that there are many more in GenBank that have not yet been identified. By automated comparison with literature they can be identified with a precision of up to 10% and a recall of up to 30%, while strongly outperforming several baselines. While these results leave substantial room for improvement, they reflect both the very imbalanced nature of the data, and the limited explicitly labelled data that is available. Overall, the obtained results show promise for the development of a new kind of approach to detecting low-quality and suspicious sequence records based on literature analysis and consistency. From a practical point of view, this will greatly help curators in identifying inconsistent records in large-scale sequence databases by highlighting records that are likely to be inconsistent with the literature.

Functional Annotations of Paralogs: A Blessing and a Curse

Gene duplication followed by mutation is a classic mechanism of neofunctionalization, producing gene families with functional diversity. In some cases, a single point mutation is sufficient to change the substrate specificity and/or the chemistry performed by an enzyme, making it difficult to accurately separate enzymes with identical functions from homologs with different functions. Because sequence similarity is often used as a basis for assigning functional annotations to genes, non-isofunctional gene families pose a great challenge for genome annotation pipelines. Here we describe how integrating evolutionary and functional information such as genome context, phylogeny, metabolic reconstruction and signature motifs may be required to correctly annotate multifunctional families. These integrative analyses can also lead to the discovery of novel gene functions, as hints from specific subgroups can guide the functional characterization of other members of the family. We demonstrate how careful manual curation processes using comparative genomics can disambiguate subgroups within large multifunctional families and discover their functions. We present the COG0720 protein family as a case study. We also discuss strategies to automate this process to improve the accuracy of genome functional annotation pipelines.