Bioinformatics Toolkit Research Articles

DeepBBQ: A Deep Learning Approach to the Protein Backbone Reconstruction.

Coarse-grained models have provided researchers with greatly improved computational efficiency in modeling structures and dynamics of biomacromolecules, but, to be practically useful, they need fast and accurate conversion methods back to the all-atom representation. Reconstruction of atomic details may also be required in the case of some experimental methods, like electron microscopy, which may provide Cα-only structures. In this contribution, we present a new method for recovery of all backbone atom positions from just the Cα coordinates. Our approach, called deepBBQ, uses a deep convolutional neural network to predict a single internal coordinate per peptide plate, based on Cα trace geometric features, and then proceeds to recalculate the cartesian coordinates based on the assumption that the peptide plate atoms lie in the same plane. Extensive comparison with similar programs shows that our solution is accurate and cost-efficient. The deepBBQ program is available as part of the open-source bioinformatics toolkit Bioshell and is free for download and the documentation is available online.

Applying rearrangement distances to enable plasmid epidemiology with pling.

Plasmids are a key vector of antibiotic resistance, but the current bioinformatics toolkit is not well suited to tracking them. The rapid structural changes seen in plasmid genomes present considerable challenges to evolutionary and epidemiological analysis. Typical approaches are either low resolution (replicon typing) or use shared k-mer content to define a genetic distance. However, this distance can both overestimate plasmid relatedness by ignoring rearrangements, and underestimate by over-penalizing gene gain/loss. Therefore a model is needed which captures the key components of how plasmid genomes evolve structurally - through gene/block gain or loss, and rearrangement. A secondary requirement is to prevent promiscuous transposable elements (TEs) leading to over-clustering of unrelated plasmids. We choose the 'Double Cut and Join Indel' (DCJ-Indel) model, in which plasmids are studied at a coarse level, as a sequence of signed integers (representing genes or aligned blocks), and the distance between two plasmids is the minimum number of rearrangement events or indels needed to transform one into the other. We show how this gives much more meaningful distances between plasmids. We introduce a software workflow pling (https://github.com/iqbal-lab-org/pling), which uses the DCJ-Indel model, to calculate distances between plasmids and then cluster them. In our approach, we combine containment distances and DCJ-Indel distances to build a TE-aware plasmid network. We demonstrate superior performance and interpretability to other plasmid clustering tools on the 'Russian Doll' dataset and a hospital transmission dataset.

SynGAP: a synteny-based toolkit for gene structure annotation polishing

Genome sequencing has become a routine task for biologists, but the challenge of gene structure annotation persists, impeding accurate genomic and genetic research. Here, we present a bioinformatics toolkit, SynGAP (Synteny-based Gene structure Annotation Polisher), which uses gene synteny information to accomplish precise and automated polishing of gene structure annotation of genomes. SynGAP offers exceptional capabilities in the improvement of gene structure annotation quality and the profiling of integrative gene synteny between species. Furthermore, an expression variation index is designed for comparative transcriptomics analysis to explore candidate genes responsible for the development of distinct traits observed in phylogenetically related species.

A Bioinformatics Toolkit for Next-Generation Sequencing in Clinical Oncology.

Next-generation sequencing (NGS) has taken on major importance in clinical oncology practice. With the advent of targeted therapies capable of effectively targeting specific genomic alterations in cancer patients, the development of bioinformatics processes has become crucial. Thus, bioinformatics pipelines play an essential role not only in the detection and in identification of molecular alterations obtained from NGS data but also in the analysis and interpretation of variants, making it possible to transform raw sequencing data into meaningful and clinically useful information. In this review, we aim to examine the multiple steps of a bioinformatics pipeline as used in current clinical practice, and we also provide an updated list of the necessary bioinformatics tools. This resource is intended to assist researchers and clinicians in their genetic data analyses, improving the precision and efficiency of these processes in clinical research and patient care.

TBtools-II: A “one for all, all for one” bioinformatics platform for biological big-data mining

Since the official release of the stand-alone bioinformatics toolkit TBtools in 2020, its superior functionality in data analysis has been demonstrated by its widespread adoption by many thousands of users and references in more than 5000 academic articles. Now, TBtools is a commonly used tool in biological laboratories. Over the past 3 years, thanks to invaluable feedback and suggestions from numerous users, we have optimized and expanded the functionality of the toolkit, leading to the development of an upgraded version—TBtools-II. In this upgrade, we have incorporated over 100 new features, such as those for comparative genomics analysis, phylogenetic analysis, and data visualization. Meanwhile, to better meet the increasing needs of personalized data analysis, we have launched the plugin mode, which enables users to develop their own plugins and manage their selection, installation, and removal according to individual needs. To date, the plugin store has amassed over 50 plugins, with more than half of them being independently developed and contributed by TBtools users. These plugins offer a range of data analysis options including co-expression network analysis, single-cell data analysis, and bulked segregant analysis sequencing data analysis. Overall, TBtools is now transforming from a stand-alone software to a comprehensive bioinformatics platform of a vibrant and cooperative community in which users are also developers and contributors. By promoting the theme “one for all, all for one”, we believe that TBtools-II will greatly benefit more biological researchers in this big-data era.

PLM-BLAST: distant homology detection based on direct comparison of sequence representations from protein language models.

The detection of homology through sequence comparison is a typical first step in the study of protein function and evolution. In this work, we explore the applicability of protein language models to this task. We introduce pLM-BLAST, a tool inspired by BLAST, that detects distant homology by comparing single-sequence representations (embeddings) derived from a protein language model, ProtT5. Our benchmarks reveal that pLM-BLAST maintains a level of accuracy on par with HHsearch for both highly similar sequences (with >50% identity) and markedly divergent sequences (with <30% identity), while being significantly faster. Additionally, pLM-BLAST stands out among other embedding-based tools due to its ability to compute local alignments. We show that these local alignments, produced by pLM-BLAST, often connect highly divergent proteins, thereby highlighting its potential to uncover previously undiscovered homologous relationships and improve protein annotation. pLM-BLAST is accessible via the MPI Bioinformatics Toolkit as a web server for searching precomputed databases (https://toolkit.tuebingen.mpg.de/tools/plmblast). It is also available as a standalone tool for building custom databases and performing batch searches (https://github.com/labstructbioinf/pLM-BLAST).

Identification of hub genes and drug-gene interactions for targeted breast cancer treatment by integrated bioinformatics analysis

Breast cancer (BC) is one of the most common cancer types in women. In addition to conventional methods for BC diagnosis, applying methods for a fast and accurate prognosis at the early stage of cancer is very meaningful for the treatment of the disease. To date, the most advanced methods are molecular diagnostics and bioinformatics. In this study, bioinformatics is applied to genetic testing for BC diagnosis; namely the R programming language combined with the bioinformatics toolkit was used to analyze gene expression levels between normal and tumor tissues in three gene expression profiles (GSE29431, GSE42568, GSE21422). The bioinformatics approaches included identification of differentially expressed genes (DEGs) and hub genes, Gen Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analyses, the construction of a protein-protein interaction (PPI) network, and module analysis. Following the completion of the hub gene selection process, coexpression and survival analysis were carried out. Finally, the GEPIA2 and DGIdb databases were utilized to verify the expression levels of hub genes and select the candidate drugs for BC, respectively. A total of 1369 DEGs was identified, including 400 upregulated DEGs and 969 downregulated DEGs. Thereafter, 10 hub genes (CDK1, CCNA2, CCNB1, CCNB2, TOP2A, KIF11, RRM2, BUB1B, CDC20, and NCAPG) were identified as potential biomarkers for BC diagnosis, prognosis, and therapy. Six screened small molecules, dexrazoxane, teniposide, amsacrine, etoposide, mitoxantrone and daunorubicin, were determined to be the new targeted drugs for BC treatment.

FAS: assessing the similarity between proteins using multi-layered feature architectures.

Protein sequence comparison is a fundamental element in the bioinformatics toolkit. When sequences are annotated with features such as functional domains, transmembrane domains, low complexity regions or secondary structure elements, the resulting feature architectures allow better informed comparisons. However, many existing schemes for scoring architecture similarities cannot cope with features arising from multiple annotation sources. Those that do fall short in the resolution of overlapping and redundant feature annotations. Here, we introduce FAS, a scoring method that integrates features from multiple annotation sources in a directed acyclic architecture graph. Redundancies are resolved as part of the architecture comparison by finding the paths through the graphs that maximize the pair-wise architecture similarity. In a large-scale evaluation on more than 10000 human-yeast ortholog pairs, architecture similarities assessed with FAS are consistently more plausible than those obtained using e-values to resolve overlaps or leaving overlaps unresolved. Three case studies demonstrate the utility of FAS on architecture comparison tasks: benchmarking of orthology assignment software, identification of functionally diverged orthologs, and diagnosing protein architecture changes stemming from faulty gene predictions. With the help of FAS, feature architecture comparisons can now be routinely integrated into these and many other applications. FAS is available as python package: https://pypi.org/project/greedyFAS/.

Metheor: Ultrafast DNA methylation heterogeneity calculation from bisulfite read alignments.

Phased DNA methylation states within bisulfite sequencing reads are valuable source of information that can be used to estimate epigenetic diversity across cells as well as epigenomic instability in individual cells. Various measures capturing the heterogeneity of DNA methylation states have been proposed for a decade. However, in routine analyses on DNA methylation, this heterogeneity is often ignored by computing average methylation levels at CpG sites, even though such information exists in bisulfite sequencing data in the form of phased methylation states, or methylation patterns. In this study, to facilitate the application of the DNA methylation heterogeneity measures in downstream epigenomic analyses, we present a Rust-based, extremely fast and lightweight bioinformatics toolkit called Metheor. As the analysis of DNA methylation heterogeneity requires the examination of pairs or groups of CpGs throughout the genome, existing softwares suffer from high computational burden, which almost make a large-scale DNA methylation heterogeneity studies intractable for researchers with limited resources. In this study, we benchmark the performance of Metheor against existing code implementations for DNA methylation heterogeneity measures in three different scenarios of simulated bisulfite sequencing datasets. Metheor was shown to dramatically reduce the execution time up to 300-fold and memory footprint up to 60-fold, while producing identical results with the original implementation, thereby facilitating a large-scale study of DNA methylation heterogeneity profiles. To demonstrate the utility of the low computational burden of Metheor, we show that the methylation heterogeneity profiles of 928 cancer cell lines can be computed with standard computing resources. With those profiles, we reveal the association between DNA methylation heterogeneity and various omics features. Source code for Metheor is at https://github.com/dohlee/metheor and is freely available under the GPL-3.0 license.

OKseqHMM: a genome-wide replication fork directionality analysis toolkit.

During each cell division, tens of thousands of DNA replication origins are co-ordinately activated to ensure the complete duplication of the human genome. However, replication fork progression can be challenged by many factors, including co-directional and head-on transcription-replication conflicts (TRC). Head-on TRCs are more dangerous for genome integrity. To study the direction of replication fork movement and TRCs, we developed a bioinformatics toolkit called OKseqHMM (https://github.com/CL-CHEN-Lab/OK-Seq, https://doi.org/10.5281/zenodo.7428883). Then, we used OKseqHMM to analyse a large number of datasets obtained by Okazaki fragment sequencing to directly measure the genome-wide replication fork directionality (RFD) and to accurately predict replication initiation and termination at a fine resolution in organisms including yeast, mouse and human. We also successfully applied our analysis to other genome-wide sequencing techniques that also contain RFD information (e.g. eSPAN, TrAEL-seq). Our toolkit can be used to predict replication initiation and fork progression direction genome-wide in a wide range of cell models and growth conditions. Comparing the replication and transcription directions allows identifying loci at risk of TRCs, particularly head-on TRCs, and investigating their role in genome instability by checking DNA damage data, which is of prime importance for human health.

A Microscopy and Bioinformatics Toolkit for High-Throughput Connectomics Studies

A Microscopy and Bioinformatics Toolkit for High-Throughput Connectomics Studies

Systematics of the Central African Spiny Reed Frog Afrixalus laevis (Anura: Hyperoliidae), with the description of two new species from the Albertine Rift.

The geographically widespread species Afrixalus laevis (Anura: Hyperoliidae) currently has a disjunct distribution in western Central Africa (Cameroon, Equatorial Guinea, Gabon, and possibly adjacent countries) and the area in and near the Albertine Rift in eastern Democratic Republic of the Congo and neighboring countries. At least two herpetologists have previously suggested that these disjunct populations represent distinct species, and herein, we utilize an integrative taxonomic approach with molecular and morphological data to reconcile the taxonomy of these spiny reed frogs. We sequenced 1554 base pairs of the 16S and RAG1 genes from 34 samples of A. laevis and one sample of A. orophilus (sympatric with eastern populations of A. laevis), and combined these data with previously sequenced GenBank Afrixalus samples via the bioinformatics toolkit SuperCRUNCH. Phylogenetic trees, dated phylogenetic analyses, and species-delimitation analyses were generated with RAxML, BEAST, and BPP, respectively. Eleven mensural characters were taken from multiple specimens of A. laevis and A. orophilus, and compared with paired t-tests and analyses of covariance. These combined results suggested populations of A. laevis in western Central Africa (Cameroon and Bioko Island, Equatorial Guinea) represent one species, whereas populations from the Albertine Rift and nearby forests represent two undescribed taxa that are sister to A. dorsimaculatus. The two new species (A. lacustris sp. nov. and A. phantasma sp. nov.) are distinguished by our phylogenetic and species-delimitation analyses, significant differences in several mensural characters, qualitative morphological differences, and by their non-overlapping elevational distribution.

Implementation of GA-VirReport, a Web-Based Bioinformatics Toolkit for Post-Entry Quarantine Screening of Virus and Viroids in Plants.

High-throughput sequencing (HTS) of host plant small RNA (sRNA) is a popular approach for plant virus and viroid detection. The major bottlenecks for implementing this approach in routine virus screening of plants in quarantine include lack of computational resources and/or expertise in command-line environments and limited availability of curated plant virus and viroid databases. We developed: (1) virus and viroid report web-based bioinformatics workflows on Galaxy Australia called GA-VirReport and GA-VirReport-Stats for detecting viruses and viroids from host plant sRNA extracts and (2) a curated higher plant virus and viroid database (PVirDB). We implemented sRNA sequencing with unique dual indexing on a set of plants with known viruses. Sequencing data were analyzed using GA-VirReport and PVirDB to validate these resources. We detected all known viruses in this pilot study with no cross-sample contamination. We then conducted a large-scale diagnosis of 105 imported plants processed at the post-entry quarantine facility (PEQ), Australia. We detected various pathogens in 14 imported plants and discovered that de novo assembly using 21–22 nt sRNA fraction and the megablast algorithm yielded better sensitivity and specificity. This study reports the successful, large-scale implementation of HTS and a user-friendly bioinformatics workflow for virus and viroid screening of imported plants at the PEQ.

The First Complete Genome Resource of a Ralstonia solanacearum Phage UAM5 from Colombia.

The First Complete Genome Resource of a Ralstonia solanacearum Phage UAM5 from Colombia.

Ribose-Map: A bioinformatics toolkit for ribonucleotide sequencing experiments

<h2>Abstract</h2> Current ribonucleotide mapping software is customized for data generated using only one type of ribonucleotide sequencing technique. To standardize the analysis of ribonucleotide sequencing experiments, we developed the bioinformatics toolkit, Ribose-Map. Through a series of analytical modules, Ribose-Map transforms raw sequencing data into summary datasets and publication-ready visualizations of results, allowing researchers to locate the genomic coordinates of ribonucleotides to single-nucleotide resolution, study the nucleotide sequence context of ribonucleotides, explore their genome-wide distribution, and identify hotspots of ribonucleotide incorporation. Ribose-Map does not require background knowledge of ribonucleotide sequencing analysis and assumes only basic command-line skills. Ribose-Map is available at https://github.com/agombolay/ribose-map.

How do I get the most out of my protein sequence using bioinformatics tools?

Biochemical and biophysical experiments are essential for uncovering the three-dimensional structure and biological role of a protein of interest. However, meaningful predictions can frequently also be made using bioinformatics resources that transfer knowledge from a well studied protein to an uncharacterized protein based on their evolutionary relatedness. These predictions are helpful in developing specific hypotheses to guide wet-laboratory experiments. Commonly used bioinformatics resources include methods to identify and predict conserved sequence motifs, protein domains, transmembrane segments, signal sequences, and secondary as well as tertiary structure. Here, several such methods available through the MPI Bioinformatics Toolkit (https://toolkit.tuebingen.mpg.de) are described and how their combined use can provide meaningful information on a protein of unknown function is demonstrated. In particular, the identification of homologs of known structure using HHpred, internal repeats using HHrepID, coiled coils using PCOILS and DeepCoil, and transmembrane segments using Quick2D are focused on.

An astonishing wealth of new proteasome homologs.

MotivationThe proteasome is the main proteolytic machine for targeted protein degradation in archaea and eukaryotes. While some bacteria also possess the proteasome, most of them contain a simpler and more specialized homolog, the heat shock locus V protease. In recent years, three further homologs of the proteasome core subunits have been characterized in prokaryotes: Anbu, BPH and connectase. With the inclusion of these members, the family of proteasome-like proteins now exhibits a range of architectural and functional forms, from the canonical proteasome, a barrel-shaped protease without pronounced intrinsic substrate specificity, to the monomeric connectase, a highly specific protein ligase.ResultsWe employed systematic sequence searches to show that we have only seen the tip of the iceberg so far and that beyond the hitherto known proteasome homologs lies a wealth of distantly related, uncharacterized homologs. We describe a total of 22 novel proteasome homologs in bacteria and archaea. Using sequence and structure analysis, we analyze their evolutionary history and assess structural differences that may modulate their function. With this initial description, we aim to stimulate the experimental investigation of these novel proteasome-like family members.Availability and implementationThe protein sequences in this study are searchable in the MPI Bioinformatics Toolkit (https://toolkit.tuebingen.mpg.de) with ProtBLAST/PSI-BLAST and with HHpred (database ‘proteasome_homologs’). The following data are available at https://data.mendeley.com/datasets/t48yhff7hs/3: (i) sequence alignments for each proteasome-like homolog, (ii) the coordinates for their structural models and (iii) a cluster-map file, which can be navigated interactively in CLANS and gives direct access to all the sequences in this study.Supplementary information Supplementary data are available at Bioinformatics online.

Mapping ribonucleotides embedded in genomic DNA to single-nucleotide resolution using Ribose-Map.

The most common nonstandard nucleotides found in genomic DNA are ribonucleotides. Although ribonucleotides are frequently incorporated into DNA by replicative DNA polymerases, very little is known about the distribution and signatures of ribonucleotides incorporated into DNA. Recent advances in high-throughput ribonucleotide sequencing can capture the exact locations of ribonucleotides in genomic DNA. Ribose-Map is a user-friendly, standardized bioinformatics toolkit for the comprehensive analysis of ribonucleotide sequencing experiments. It allows researchers to map the locations of ribonucleotides in DNA to single-nucleotide resolution and identify biological signatures of ribonucleotide incorporation. In addition, it can be applied to data generated using any currently available high-throughput ribonucleotide sequencing technique, thus standardizing the analysis of ribonucleotide sequencing experiments and allowing direct comparisons of results. This protocol describes in detail how to use Ribose-Map to analyze ribonucleotide sequencing data, including preparing the reads for analysis, locating the genomic coordinates of ribonucleotides, exploring the genome-wide distribution of ribonucleotides, determining the nucleotide sequence context of ribonucleotides and identifying hotspots of ribonucleotide incorporation. Ribose-Map does not require background knowledge of ribonucleotide sequencing analysis and assumes only basic command-line skills. The protocol requires less than 3 h of computing time for most datasets and ~30 min of hands-on time. Ribose-Map is available at https://github.com/agombolay/ribose-map .

INSaFLU-TELE-Vir: an open web-based bioinformatics suite for influenza and SARS-CoV-2 genome-based surveillance

A new era of virus surveillance is emerging based on the real-time monitoring of virus evolution at whole-genome scale (World Health Organization 2021). Although national and international health authorities have strongly recommended this technological transition, especially for influenza and SARS-CoV-2 (World Health Organization 2021, Revez et al. 2017), the implementation of genomic surveillance can be particularly challenging due to the lack of bioinformatics infrastructures and/or expertise to process and interpret next-generation sequencing (NGS) data (Oakeson et al. 2017). We developed and implemented INSaFLU-TELE-Vir platform (https://insaflu.insa.pt/) (Borges et al. 2018), which is an influenza- and SARS-CoV-2-oriented bioinformatics free web-based suite that handles primary NGS data (reads) towards the automatic generation of the main “genetic requests'' for effective and timely laboratory surveillance. By handling NGS data collected from any amplicon-based schema (making it applicable for other pathogens), INSaFLU-TELE-Vir enables any laboratory to perform multi-step and intensive bioinformatics analyses in a user-oriented manner without requiring advanced training. INSaFLU-TELE-Vir handles NGS data collected from distinct sequencing technologies (Illumina, Ion Torrent and Oxford Nanopore Technologies), with the possibility of constructing comparative analyses using different technologies. It gives access to user-restricted sample databases and project management, being a transparent and flexible tool specifically designed to automatically update project outputs as more samples are uploaded. Data integration is thus cumulative and scalable, fitting the need for both routine surveillance and outbreak investigation activities. The bioinformatics pipeline consists of six core steps: read quality analysis and improvement, human betacoronaviruses (including SARS-CoV-2 Pango lineages) and influenza type/subtype classification, mutation detection and consensus generation, coverage analysis, alignment/phylogeny, intra-host minor variant detection (and automatic detection of putative mixed infections). read quality analysis and improvement, human betacoronaviruses (including SARS-CoV-2 Pango lineages) and influenza type/subtype classification, mutation detection and consensus generation, coverage analysis, alignment/phylogeny, intra-host minor variant detection (and automatic detection of putative mixed infections). The multiple outputs are provided in nomenclature-stable and standardized formats that can be visualized and explored in situ or through multiple compatible downstream applications for fine-tuned data analysis. Novel features are being implemented into the INSaFLU-TELE-Vir bioinformatics toolkit as part of the OHEJP TELE-Vir (https://onehealthejp.eu/jrp-tele-vir/) project, including rapid detection of selected genotype-phenotype associations, and enhanced geotemporal data visualization. All the code is available in github (https://github.com/INSaFLU) with the possibility of a local docker installation (https://github.com/INSaFLU/docker). A detailed documentation and tutorial is also available (https://insaflu.readthedocs.io/en/latest/). In summary, INSaFLU supplies public health laboratories and researchers with an open and user-friendly framework, potentiating a strengthened and timely multi-country genome-based virus surveillance.

Combined Transcriptomic and Proteomic Analysis of Perk Toxicity Pathways.

In Drosophila, endoplasmic reticulum (ER) stress activates the protein kinase R-like endoplasmic reticulum kinase (dPerk). dPerk can also be activated by defective mitochondria in fly models of Parkinson’s disease caused by mutations in pink1 or parkin. The Perk branch of the unfolded protein response (UPR) has emerged as a major toxic process in neurodegenerative disorders causing a chronic reduction in vital proteins and neuronal death. In this study, we combined microarray analysis and quantitative proteomics analysis in adult flies overexpressing dPerk to investigate the relationship between the transcriptional and translational response to dPerk activation. We identified tribbles and Heat shock protein 22 as two novel Drosophila activating transcription factor 4 (dAtf4) regulated transcripts. Using a combined bioinformatics tool kit, we demonstrated that the activation of dPerk leads to translational repression of mitochondrial proteins associated with glutathione and nucleotide metabolism, calcium signalling and iron-sulphur cluster biosynthesis. Further efforts to enhance these translationally repressed dPerk targets might offer protection against Perk toxicity.