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1. HiCARN: resolution enhancement of Hi-C data using cascading residual networks

   https://doi.org/10.1093/bioinformatics/btac156  

   Hicks, Parker ; Oluwadare, Oluwatosin ; Robinson, ed., Peter ( March 2022 , Bioinformatics)

   High throughput chromosome conformation capture (Hi-C) contact matrices are used to predict 3D chromatin structures in eukaryotic cells. High-resolution Hi-C data are less available than low-resolution Hi-C data due to sequencing costs but provide greater insight into the intricate details of 3D chromatin structures such as enhancer–promoter interactions and sub-domains. To provide a cost-effective solution to high-resolution Hi-C data collection, deep learning models are used to predict high-resolution Hi-C matrices from existing low-resolution matrices across multiple cell types.

   Here, we present two Cascading Residual Networks called HiCARN-1 and HiCARN-2, a convolutional neural network and a generative adversarial network, that use a novel framework of cascading connections throughout the network for Hi-C contact matrix prediction from low-resolution data. Shown by image evaluation and Hi-C reproducibility metrics, both HiCARN models, overall, outperform state-of-the-art Hi-C resolution enhancement algorithms in predictive accuracy for both human and mouse 1/16, 1/32, 1/64 and 1/100 downsampled high-resolution Hi-C data. Also, validation by extracting topologically associating domains, chromosome 3D structure and chromatin loop predictions from the enhanced data shows that HiCARN can proficiently reconstruct biologically significant regions.

   HiCARN can be accessed and utilized as an open-sourced software at: https://github.com/OluwadareLab/HiCARN and is also available as amore »containerized application that can be run on any platform.

   Supplementary data are available at Bioinformatics online.

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2. Rare and potentially pathogenic variants in hydroxycarboxylic acid receptor genes identified in breast cancer cases

   https://doi.org/10.1186/s12920-021-01126-3  

   McGuire Sams, Cierla ; Shepp, Kasey ; Pugh, Jada ; Bishop, Madison R. ; Merner, Nancy D. ( December 2021 , BMC Medical Genomics)

   Three genes clustered together on chromosome 12 comprise a group of hydroxycarboxylic acid receptors (HCARs):HCAR1,HCAR2, andHCAR3. These paralogous genes encode different G-protein coupled receptors responsible for detecting glycolytic metabolites and controlling fatty acid oxidation. Though better known for regulating lipid metabolism in adipocytes, more recently, HCARs have been functionally associated with breast cancer proliferation/survival;HCAR2has been described as a tumor suppressor andHCAR1andHCAR3as oncogenes. Thus, we sought to identify germline variants inHCAR1,HCAR2,andHCAR3that could potentially be associated with breast cancer risk.

   Two different cohorts of breast cancer cases were investigated, the Alabama Hereditary Cancer Cohort and The Cancer Genome Atlas, which were analyzed through nested PCRs/Sanger sequencing and whole-exome sequencing, respectively. All datasets were screened for rare, non-synonymous coding variants.

   Variants were identified in both breast cancer cohorts, some of which appeared to be associated with breast cancer BC risk, includingHCAR1c.58C > G (p.P20A),HCAR2c.424C > T (p.R142W),HCAR2c.517_518delinsAC (p.G173T),HCAR2c.1036A > G (p.M346V),HCAR2c.1086_1090del (p.P363Nfs*26),HCAR3c.560G > A (p.R187Q), andHCAR3c.1117delC (p.Q373Kfs*82). Additionally,HCAR2c.515C > T (p.S172L), a previously identified loss-of-function variant, was identified.

   Due to the important role of HCARs in breast cancer, it is vital to understand how these genetic variants play a role in breast cancer risk and proliferation and their consequences on treatment strategies. Additional studies will be needed to validate these findings. Nevertheless,more »the identification of these potentially pathogenic variants supports the need to investigate their functional consequences.

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3. Viral quasispecies reconstruction via tensor factorization with successive read removal

   https://doi.org/10.1093/bioinformatics/bty291  

   Ahn, Soyeon ; Ke, Ziqi ; Vikalo, Haris ( June 2018 , Bioinformatics)

   As RNA viruses mutate and adapt to environmental changes, often developing resistance to anti-viral vaccines and drugs, they form an ensemble of viral strains––a viral quasispecies. While high-throughput sequencing (HTS) has enabled in-depth studies of viral quasispecies, sequencing errors and limited read lengths render the problem of reconstructing the strains and estimating their spectrum challenging. Inference of viral quasispecies is difficult due to generally non-uniform frequencies of the strains, and is further exacerbated when the genetic distances between the strains are small.

   This paper presents TenSQR, an algorithm that utilizes tensor factorization framework to analyze HTS data and reconstruct viral quasispecies characterized by highly uneven frequencies of its components. Fundamentally, TenSQR performs clustering with successive data removal to infer strains in a quasispecies in order from the most to the least abundant one; every time a strain is inferred, sequencing reads generated from that strain are removed from the dataset. The proposed successive strain reconstruction and data removal enables discovery of rare strains in a population and facilitates detection of deletions in such strains. Results on simulated datasets demonstrate that TenSQR can reconstruct full-length strains having widely different abundances, generally outperforming state-of-the-art methods at diversities 1–10% and detecting longmore »deletions even in rare strains. A study on a real HIV-1 dataset demonstrates that TenSQR outperforms competing methods in experimental settings as well. Finally, we apply TenSQR to analyze a Zika virus sample and reconstruct the full-length strains it contains.

   TenSQR is available at https://github.com/SoYeonA/TenSQR.

   Supplementary data are available at Bioinformatics online.

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4. HiFine: integrating Hi-C-based and shotgun-based methods to refine binning of metagenomic contigs

   https://doi.org/10.1093/bioinformatics/btac295  

   Du, Yuxuan ; Sun, Fengzhu ; Birol, ed., Inanc ( April 2022 , Bioinformatics)

   Metagenomic binning aims to retrieve microbial genomes directly from ecosystems by clustering metagenomic contigs assembled from short reads into draft genomic bins. Traditional shotgun-based binning methods depend on the contigs’ composition and abundance profiles and are impaired by the paucity of enough samples to construct reliable co-abundance profiles. When applied to a single sample, shotgun-based binning methods struggle to distinguish closely related species only using composition information. As an alternative binning approach, Hi-C-based binning employs metagenomic Hi-C technique to measure the proximity contacts between metagenomic fragments. However, spurious inter-species Hi-C contacts inevitably generated by incorrect ligations of DNA fragments between species link the contigs from varying genomes, weakening the purity of final draft genomic bins. Therefore, it is imperative to develop a binning pipeline to overcome the shortcomings of both types of binning methods on a single sample.

   We develop HiFine, a novel binning pipeline to refine the binning results of metagenomic contigs by integrating both Hi-C-based and shotgun-based binning tools. HiFine designs a strategy of fragmentation for the original bin sets derived from the Hi-C-based and shotgun-based binning methods, which considerably increases the purity of initial bins, followed by merging fragmented bins and recruiting unbinned contigs. We demonstratemore »that HiFine significantly improves the existing binning results of both types of binning methods and achieves better performance in constructing species genomes on publicly available datasets. To the best of our knowledge, HiFine is the first pipeline to integrate different types of tools for the binning of metagenomic contigs.

   HiFine is available at https://github.com/dyxstat/HiFine.

   Supplementary data are available at Bioinformatics online.

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5. miniMDS: 3D structural inference from high-resolution Hi-C data

   https://doi.org/10.1093/bioinformatics/btx271  

   Rieber, Lila ; Mahony, Shaun ( July 2017 , Bioinformatics)

   Recent experiments have provided Hi-C data at resolution as high as 1 kbp. However, 3D structural inference from high-resolution Hi-C datasets is often computationally unfeasible using existing methods.

   We have developed miniMDS, an approximation of multidimensional scaling (MDS) that partitions a Hi-C dataset, performs high-resolution MDS separately on each partition, and then reassembles the partitions using low-resolution MDS. miniMDS is faster, more accurate, and uses less memory than existing methods for inferring the human genome at high resolution (10 kbp).

   A Python implementation of miniMDS is available on GitHub: https://github.com/seqcode/miniMDS.

   Supplementary data are available at Bioinformatics online.