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1. ToxCodAn: a new toxin annotator and guide to venom gland transcriptomics

   https://doi.org/10.1093/bib/bbab095  

   Nachtigall, Pedro G. ; Rautsaw, Rhett M. ; Ellsworth, Schyler A. ; Mason, Andrew J. ; Rokyta, Darin R. ; Parkinson, Christopher L. ; Junqueira-de-Azevedo, Inácio L. M. ( April 2021 , Briefings in Bioinformatics)

   Next-generation sequencing has become exceedingly common and has transformed our ability to explore nonmodel systems. In particular, transcriptomics has facilitated the study of venom and evolution of toxins in venomous lineages; however, many challenges remain. Primarily, annotation of toxins in the transcriptome is a laborious and time-consuming task. Current annotation software often fails to predict the correct coding sequence and overestimates the number of toxins present in the transcriptome. Here, we present ToxCodAn, a python script designed to perform precise annotation of snake venom gland transcriptomes. We test ToxCodAn with a set of previously curated transcriptomes and compare the results to other annotators. In addition, we provide a guide for venom gland transcriptomics to facilitate future research and use Bothrops alternatus as a case study for ToxCodAn and our guide.

   Our analysis reveals that ToxCodAn provides precise annotation of toxins present in the transcriptome of venom glands of snakes. Comparison with other annotators demonstrates that ToxCodAn has better performance with regard to run time ($>20x$ faster), coding sequence prediction ($>3x$ more accurate) and the number of toxins predicted (generating $>4x$ less false positives). In this sense, ToxCodAn is a valuable resource for toxin annotation. The ToxCodAn framework can be expanded in the future to work with other venomous lineages and detect novel toxins.

    

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2. Tau-typing: a Nextflow pipeline for finding the best phylogenetic markers in the genome for molecular typing of microbial species

   https://doi.org/10.1093/bioinformatics/btad425  

   Seabolt, Matthew H. ; Boddapati, Arun K. ; Forstedt, Joshua J. ; Konstantinidis, Konstantinos T. ; Schwartz, ed., Russell ( July 2023 , Bioinformatics)

   Tau-typing is an integrated analysis pipeline for identifying genes or genomic segments whose phylogenetic resolving power most closely resembles the genome-wide resolving power of an input collection of genomes using the Kendall Tau rank correlation statistic. The pipeline is implemented in Nextflow and uses Docker and Singularity containers to ensure reliable scalability and reproducibility of results. This pipeline is particularly suitable for organisms for which whole-genome sequencing remains unaffordable or unscalable for routine applications, such as protozoan parasites which are not amenable to laboratory culture-based methods.

   Tau-typing is freely available at https://github.com/hseabolt/tautyping. The pipeline is implemented in Nextflow with Singularity support.

    

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3. Markov chains improve the significance computation of overlapping genome annotations

   https://doi.org/10.1093/bioinformatics/btac255  

   Gafurov, Askar ; Brejová, Broňa ; Medvedev, Paul ( June 2022 , Bioinformatics)

   Genome annotations are a common way to represent genomic features such as genes, regulatory elements or epigenetic modifications. The amount of overlap between two annotations is often used to ascertain if there is an underlying biological connection between them. In order to distinguish between true biological association and overlap by pure chance, a robust measure of significance is required. One common way to do this is to determine if the number of intervals in the reference annotation that intersect the query annotation is statistically significant. However, currently employed statistical frameworks are often either inefficient or inaccurate when computing P-values on the scale of the whole human genome.

   We show that finding the P-values under the typically used ‘gold’ null hypothesis is NP-hard. This motivates us to reformulate the null hypothesis using Markov chains. To be able to measure the fidelity of our Markovian null hypothesis, we develop a fast direct sampling algorithm to estimate the P-value under the gold null hypothesis. We then present an open-source software tool MCDP that computes the P-values under the Markovian null hypothesis in O(m2+n) time and O(m) memory, where m and n are the numbers of intervals in the reference and query annotations, respectively. Notably, MCDP runtime and memory usage are independent from the genome length, allowing it to outperform previous approaches in runtime and memory usage by orders of magnitude on human genome annotations, while maintaining the same level of accuracy.

   The software is available at https://github.com/fmfi-compbio/mc-overlaps. All data for reproducibility are available at https://github.com/fmfi-compbio/mc-overlaps-reproducibility.

   Supplementary data are available at Bioinformatics online.

    

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4. METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks

   https://doi.org/10.1186/s40168-021-01213-8  

   Zhou, Zhichao ; Tran, Patricia Q. ; Breister, Adam M. ; Liu, Yang ; Kieft, Kristopher ; Cowley, Elise S. ; Karaoz, Ulas ; Anantharaman, Karthik ( February 2022 , Microbiome)

   Advances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent; however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and microbial contributions to biogeochemical cycling.

   We present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry studies using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the microbiome, potential microbial metabolic handoffs and metabolite exchange, reconstruction of functional networks, and determination of microbial contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, community-scale microbial functional networks using a newly defined metric “MW-score” (metabolic weight score), and metabolic Sankey diagrams. METABOLIC takes ~ 3 h with 40 CPU threads to process ~ 100 genomes and corresponding metagenomic reads within which the most compute-demanding part of hmmsearch takes ~ 45 min, while it takes ~ 5 h to complete hmmsearch for ~ 3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut.

   METABOLIC enables the consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available under GPLv3 athttps://github.com/AnantharamanLab/METABOLIC.

    

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5. A map of cis-regulatory modules and constituent transcription factor binding sites in 80% of the mouse genome

   https://doi.org/10.1186/s12864-022-08933-7  

   Ni, Pengyu ; Wilson, David ; Su, Zhengchang ( October 2022 , BMC Genomics)

   Mouse is probably the most important model organism to study mammal biology and human diseases. A better understanding of the mouse genome will help understand the human genome, biology and diseases. However, despite the recent progress, the characterization of the regulatory sequences in the mouse genome is still far from complete, limiting its use to understand the regulatory sequences in the human genome.

   Here, by integrating binding peaks in ~ 9,000 transcription factor (TF) ChIP-seq datasets that cover 79.9% of the mouse mappable genome using an efficient pipeline, we were able to partition these binding peak-covered genome regions into acis-regulatory module (CRM) candidate (CRMC) set and a non-CRMC set. The CRMCs contain 912,197 putative CRMs and 38,554,729 TF binding sites (TFBSs) islands, covering 55.5% and 24.4% of the mappable genome, respectively. The CRMCs tend to be under strong evolutionary constraints, indicating that they are likelycis-regulatory; while the non-CRMCs are largely selectively neutral, indicating that they are unlikelycis-regulatory. Based on evolutionary profiles of the genome positions, we further estimated that 63.8% and 27.4% of the mouse genome might code for CRMs and TFBSs, respectively.

   Validation using experimental data suggests that at least most of the CRMCs are authentic. Thus, this unprecedentedly comprehensive map of CRMs and TFBSs can be a good resource to guide experimental studies of regulatory genomes in mice and humans.

    

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