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1. Drosophila Evolution over Space and Time (DEST): A New Population Genomics Resource

   https://doi.org/10.1093/molbev/msab259  

   Kapun, Martin ; Nunez, Joaquin C ; Bogaerts-Márquez, María ; Murga-Moreno, Jesús ; Paris, Margot ; Outten, Joseph ; Coronado-Zamora, Marta ; Tern, Courtney ; Rota-Stabelli, Omar ; Guerreiro, Maria P ; et al ( September 2021 , Molecular Biology and Evolution)

   Nielsen, Rasmus (Ed.)

   Abstract Drosophila melanogaster is a leading model in population genetics and genomics, and a growing number of whole-genome data sets from natural populations of this species have been published over the last years. A major challenge is the integration of disparate data sets, often generated using different sequencing technologies and bioinformatic pipelines, which hampers our ability to address questions about the evolution of this species. Here we address these issues by developing a bioinformatics pipeline that maps pooled sequencing (Pool-Seq) reads from D. melanogaster to a hologenome consisting of fly and symbiont genomes and estimates allele frequencies using either a heuristic (PoolSNP) or a probabilistic variant caller (SNAPE-pooled). We use this pipeline to generate the largest data repository of genomic data available for D. melanogaster to date, encompassing 271 previously published and unpublished population samples from over 100 locations in >20 countries on four continents. Several of these locations have been sampled at different seasons across multiple years. This data set, which we call Drosophila Evolution over Space and Time (DEST), is coupled with sampling and environmental metadata. A web-based genome browser and web portal provide easy access to the SNP data set. We further provide guidelines on how to use Pool-Seq data for model-based demographic inference. Our aim is to provide this scalable platform as a community resource which can be easily extended via future efforts for an even more extensive cosmopolitan data set. Our resource will enable population geneticists to analyze spatiotemporal genetic patterns and evolutionary dynamics of D. melanogaster populations in unprecedented detail. 

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2. High-performance single-cell gene regulatory network inference at scale: the Inferelator 3.0

   https://doi.org/10.1093/bioinformatics/btac117  

   Skok Gibbs, Claudia ; Jackson, Christopher A. ; Saldi, Giuseppe-Antonio ; Tjärnberg, Andreas ; Shah, Aashna ; Watters, Aaron ; De Veaux, Nicholas ; Tchourine, Konstantine ; Yi, Ren ; Hamamsy, Tymor ; et al ( February 2022 , Bioinformatics)

   Gene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. Methods for inferring and reconstructing networks from genomics data have evolved rapidly over the last decade in response to advances in sequencing technology and machine learning. The scale of data collection has increased dramatically; the largest genome-wide gene expression datasets have grown from thousands of measurements to millions of single cells, and new technologies are on the horizon to increase to tens of millions of cells and above.

   In this work, we present the Inferelator 3.0, which has been significantly updated to integrate data from distinct cell types to learn context-specific regulatory networks and aggregate them into a shared regulatory network, while retaining the functionality of the previous versions. The Inferelator is able to integrate the largest single-cell datasets and learn cell-type-specific gene regulatory networks. Compared to other network inference methods, the Inferelator learns new and informative Saccharomyces cerevisiae networks from single-cell gene expression data, measured by recovery of a known gold standard. We demonstrate its scaling capabilities by learning networks for multiple distinct neuronal and glial cell types in the developing Mus musculus brain at E18 from a large (1.3 million) single-cell gene expression dataset with paired single-cell chromatin accessibility data.

   The inferelator software is available on GitHub (https://github.com/flatironinstitute/inferelator) under the MIT license and has been released as python packages with associated documentation (https://inferelator.readthedocs.io/).

   Supplementary data are available at Bioinformatics online.

    

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3. Unification of miRNA and isomiR research: the mirGFF3 format and the mirtop API

   https://doi.org/10.1093/bioinformatics/btz675  

   Desvignes, Thomas ; Loher, Phillipe ; Eilbeck, Karen ; Ma, Jeffery ; Urgese, Gianvito ; Fromm, Bastian ; Sydes, Jason ; Aparicio-Puerta, Ernesto ; Barrera, Victor ; Espín, Roderic ; et al ( August 2019 , Bioinformatics)

   MicroRNAs (miRNAs) are small RNA molecules (∼22 nucleotide long) involved in post-transcriptional gene regulation. Advances in high-throughput sequencing technologies led to the discovery of isomiRs, which are miRNA sequence variants. While many miRNA-seq analysis tools exist, the diversity of output formats hinders accurate comparisons between tools and precludes data sharing and the development of common downstream analysis methods.

   To overcome this situation, we present here a community-based project, miRNA Transcriptomic Open Project (miRTOP) working towards the optimization of miRNA analyses. The aim of miRTOP is to promote the development of downstream isomiR analysis tools that are compatible with existing detection and quantification tools. Based on the existing GFF3 format, we first created a new standard format, mirGFF3, for the output of miRNA/isomiR detection and quantification results from small RNA-seq data. Additionally, we developed a command line Python tool, mirtop, to create and manage the mirGFF3 format. Currently, mirtop can convert into mirGFF3 the outputs of commonly used pipelines, such as seqbuster, isomiR-SEA, sRNAbench, Prost! as well as BAM files. Some tools have also incorporated the mirGFF3 format directly into their code, such as, miRge2.0, IsoMIRmap and OptimiR. Its open architecture enables any tool or pipeline to output or convert results into mirGFF3. Collectively, this isomiR categorization system, along with the accompanying mirGFF3 and mirtop API, provide a comprehensive solution for the standardization of miRNA and isomiR annotation, enabling data sharing, reporting, comparative analyses and benchmarking, while promoting the development of common miRNA methods focusing on downstream steps of miRNA detection, annotation and quantification.

   https://github.com/miRTop/mirGFF3/ and https://github.com/miRTop/mirtop.

   desvignes@uoneuro.uoregon.edu or lpantano@iscb.org

   Supplementary data are available at Bioinformatics online.

    

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4. Network-based drug sensitivity prediction

   https://doi.org/10.1186/s12920-020-00829-3  

   Ahmed, Khandakar Tanvir ; Park, Sunho ; Jiang, Qibing ; Yeu, Yunku ; Hwang, TaeHyun ; Zhang, Wei ( December 2020 , BMC Medical Genomics)

   null (Ed.)

   Abstract Background Drug sensitivity prediction and drug responsive biomarker selection on high-throughput genomic data is a critical step in drug discovery. Many computational methods have been developed to serve this purpose including several deep neural network models. However, the modular relations among genomic features have been largely ignored in these methods. To overcome this limitation, the role of the gene co-expression network on drug sensitivity prediction is investigated in this study. Methods In this paper, we first introduce a network-based method to identify representative features for drug response prediction by using the gene co-expression network. Then, two graph-based neural network models are proposed and both models integrate gene network information directly into neural network for outcome prediction. Next, we present a large-scale comparative study among the proposed network-based methods, canonical prediction algorithms (i.e., Elastic Net, Random Forest, Partial Least Squares Regression, and Support Vector Regression), and deep neural network models for drug sensitivity prediction. All the source code and processed datasets in this study are available at https://github.com/compbiolabucf/drug-sensitivity-prediction . Results In the comparison of different feature selection methods and prediction methods on a non-small cell lung cancer (NSCLC) cell line RNA-seq gene expression dataset with 50 different drug treatments, we found that (1) the network-based feature selection method improves the prediction performance compared to Pearson correlation coefficients; (2) Random Forest outperforms all the other canonical prediction algorithms and deep neural network models; (3) the proposed graph-based neural network models show better prediction performance compared to deep neural network model; (4) the prediction performance is drug dependent and it may relate to the drug’s mechanism of action. Conclusions Network-based feature selection method and prediction models improve the performance of the drug response prediction. The relations between the genomic features are more robust and stable compared to the correlation between each individual genomic feature and the drug response in high dimension and low sample size genomic datasets. 

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5. From telomere to telomere: The transcriptional and epigenetic state of human repeat elements

   https://doi.org/10.1126/science.abk3112  

   Hoyt, Savannah J. ; Storer, Jessica M. ; Hartley, Gabrielle A. ; Grady, Patrick G. ; Gershman, Ariel ; de Lima, Leonardo G. ; Limouse, Charles ; Halabian, Reza ; Wojenski, Luke ; Rodriguez, Matias ; et al ( April 2022 , Science)

   INTRODUCTION Transposable elements (TEs), repeat expansions, and repeat-mediated structural rearrangements play key roles in chromosome structure and species evolution, contribute to human genetic variation, and substantially influence human health through copy number variants, structural variants, insertions, deletions, and alterations to gene transcription and splicing. Despite their formative role in genome stability, repetitive regions have been relegated to gaps and collapsed regions in human genome reference GRCh38 owing to the technological limitations during its development. The lack of linear sequence in these regions, particularly in centromeres, resulted in the inability to fully explore the repeat content of the human genome in the context of both local and regional chromosomal environments. RATIONALE Long-read sequencing supported the complete, telomere-to-telomere (T2T) assembly of the pseudo-haploid human cell line CHM13. This resource affords a genome-scale assessment of all human repetitive sequences, including TEs and previously unknown repeats and satellites, both within and outside of gaps and collapsed regions. Additionally, a complete genome enables the opportunity to explore the epigenetic and transcriptional profiles of these elements that are fundamental to our understanding of chromosome structure, function, and evolution. Comparative analyses reveal modes of repeat divergence, evolution, and expansion or contraction with locus-level resolution. RESULTS We implemented a comprehensive repeat annotation workflow using previously known human repeats and de novo repeat modeling followed by manual curation, including assessing overlaps with gene annotations, segmental duplications, tandem repeats, and annotated repeats. Using this method, we developed an updated catalog of human repetitive sequences and refined previous repeat annotations. We discovered 43 previously unknown repeats and repeat variants and characterized 19 complex, composite repetitive structures, which often carry genes, across T2T-CHM13. Using precision nuclear run-on sequencing (PRO-seq) and CpG methylated sites generated from Oxford Nanopore Technologies long-read sequencing data, we assessed RNA polymerase engagement across retroelements genome-wide, revealing correlations between nascent transcription, sequence divergence, CpG density, and methylation. These analyses were extended to evaluate RNA polymerase occupancy for all repeats, including high-density satellite repeats that reside in previously inaccessible centromeric regions of all human chromosomes. Moreover, using both mapping-dependent and mapping-independent approaches across early developmental stages and a complete cell cycle time series, we found that engaged RNA polymerase across satellites is low; in contrast, TE transcription is abundant and serves as a boundary for changes in CpG methylation and centromere substructure. Together, these data reveal the dynamic relationship between transcriptionally active retroelement subclasses and DNA methylation, as well as potential mechanisms for the derivation and evolution of new repeat families and composite elements. Focusing on the emerging T2T-level assembly of the HG002 X chromosome, we reveal that a high level of repeat variation likely exists across the human population, including composite element copy numbers that affect gene copy number. Additionally, we highlight the impact of repeats on the structural diversity of the genome, revealing repeat expansions with extreme copy number differences between humans and primates while also providing high-confidence annotations of retroelement transduction events. CONCLUSION The comprehensive repeat annotations and updated repeat models described herein serve as a resource for expanding the compendium of human genome sequences and reveal the impact of specific repeats on the human genome. In developing this resource, we provide a methodological framework for assessing repeat variation within and between human genomes. The exhaustive assessment of the transcriptional landscape of repeats, at both the genome scale and locally, such as within centromeres, sets the stage for functional studies to disentangle the role transcription plays in the mechanisms essential for genome stability and chromosome segregation. Finally, our work demonstrates the need to increase efforts toward achieving T2T-level assemblies for nonhuman primates and other species to fully understand the complexity and impact of repeat-derived genomic innovations that define primate lineages, including humans. Telomere-to-telomere assembly of CHM13 supports repeat annotations and discoveries. The human reference T2T-CHM13 filled gaps and corrected collapsed regions (triangles) in GRCh38. Combining long read–based methylation calls, PRO-seq, and multilevel computational methods, we provide a compendium of human repeats, define retroelement expression and methylation profiles, and delineate locus-specific sites of nascent transcription genome-wide, including previously inaccessible centromeres. SINE, short interspersed element; SVA, SINE–variable number tandem repeat– Alu ; LINE, long interspersed element; LTR, long terminal repeat; TSS, transcription start site; pA, xxxxxxxxxxxxxxxx. 

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