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1. Heritability estimation and differential analysis of count data with generalized linear mixed models in genomic sequencing studies

   https://doi.org/10.1093/bioinformatics/bty644  

   Sun, Shiquan ; Zhu, Jiaqiang ; Mozaffari, Sahar ; Ober, Carole ; Chen, Mengjie ; Zhou, Xiang ; Birol, ed., Inanc ( July 2018 , Bioinformatics)

   Genomic sequencing studies, including RNA sequencing and bisulfite sequencing studies, are becoming increasingly common and increasingly large. Large genomic sequencing studies open doors for accurate molecular trait heritability estimation and powerful differential analysis. Heritability estimation and differential analysis in sequencing studies requires the development of statistical methods that can properly account for the count nature of the sequencing data and that are computationally efficient for large datasets.

   Here, we develop such a method, PQLseq (Penalized Quasi-Likelihood for sequencing count data), to enable effective and efficient heritability estimation and differential analysis using the generalized linear mixed model framework. With extensive simulations and comparisons to previous methods, we show that PQLseq is the only method currently available that can produce unbiased heritability estimates for sequencing count data. In addition, we show that PQLseq is well suited for differential analysis in large sequencing studies, providing calibrated type I error control and more power compared to the standard linear mixed model methods. Finally, we apply PQLseq to perform gene expression heritability estimation and differential expression analysis in a large RNA sequencing study in the Hutterites.

   PQLseq is implemented as an R package with source code freely available at www.xzlab.org/software.html and https://cran.r-project.org/web/packages/PQLseq/index.html.

   Supplementary data are available at Bioinformatics online.

    

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2. Comparative genome analysis using sample-specific string detection in accurate long reads

   https://doi.org/10.1093/bioadv/vbab005  

   Khorsand, Parsoa ; Denti, Luca ; Bonizzoni, Paola ; Chikhi, Rayan ; Hormozdiari, Fereydoun ( January 2021 , Bioinformatics Advances)

   Stamatakis, Alexandros (Ed.)

   Abstract Motivation Comparative genome analysis of two or more whole-genome sequenced (WGS) samples is at the core of most applications in genomics. These include the discovery of genomic differences segregating in populations, case-control analysis in common diseases and diagnosing rare disorders. With the current progress of accurate long-read sequencing technologies (e.g. circular consensus sequencing from PacBio sequencers), we can dive into studying repeat regions of the genome (e.g. segmental duplications) and hard-to-detect variants (e.g. complex structural variants). Results We propose a novel framework for comparative genome analysis through the discovery of strings that are specific to one genome (‘samples-specific’ strings). We have developed a novel, accurate and efficient computational method for the discovery of sample-specific strings between two groups of WGS samples. The proposed approach will give us the ability to perform comparative genome analysis without the need to map the reads and is not hindered by shortcomings of the reference genome and mapping algorithms. We show that the proposed approach is capable of accurately finding sample-specific strings representing nearly all variation (>98%) reported across pairs or trios of WGS samples using accurate long reads (e.g. PacBio HiFi data). Availability and implementation Data, code and instructions for reproducing the results presented in this manuscript are publicly available at https://github.com/Parsoa/PingPong. Supplementary information Supplementary data are available at Bioinformatics Advances online. 

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3. SNIKT: sequence-independent adapter identification and removal in long-read shotgun sequencing data

   https://doi.org/10.1093/bioinformatics/btac389  

   Ranjan, Piyush ; Brown, Christopher A ; Erb-Downward, John R ; Dickson, Robert P ( June 2022 , Bioinformatics)

   Alkan, Can (Ed.)

   Abstract Summary Here, we introduce SNIKT, a command-line tool for sequence-independent visual confirmation and input-assisted removal of adapter contamination in whole-genome shotgun or metagenomic shotgun long-read sequencing DNA or RNA data. Availability and Implementation SNIKT is implemented in R and is compatible with Unix-like platforms. The source code, along with documentation, is freely available under an MIT license at https://github.com/piyuranjan/SNIKT. Supplementary information Supplementary data are available at Bioinformatics online. 

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4. GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure

   https://doi.org/10.1186/s12859-022-04629-7  

   Hadish, John A. ; Biggs, Tyler D. ; Shealy, Benjamin T. ; Bender, M. Reed ; McKnight, Coleman B. ; Wytko, Connor ; Smith, Melissa C. ; Feltus, F. Alex ; Honaas, Loren ; Ficklin, Stephen P. ( December 2022 , BMC Bioinformatics)

   Abstract Background Quantification of gene expression from RNA-seq data is a prerequisite for transcriptome analysis such as differential gene expression analysis and gene co-expression network construction. Individual RNA-seq experiments are larger and combining multiple experiments from sequence repositories can result in datasets with thousands of samples. Processing hundreds to thousands of RNA-seq data can result in challenges related to data management, access to sufficient computational resources, navigation of high-performance computing (HPC) systems, installation of required software dependencies, and reproducibility. Processing of larger and deeper RNA-seq experiments will become more common as sequencing technology matures. Results GEMmaker, is a nf-core compliant, Nextflow workflow, that quantifies gene expression from small to massive RNA-seq datasets. GEMmaker ensures results are highly reproducible through the use of versioned containerized software that can be executed on a single workstation, institutional compute cluster, Kubernetes platform or the cloud. GEMmaker supports popular alignment and quantification tools providing results in raw and normalized formats. GEMmaker is unique in that it can scale to process thousands of local or remote stored samples without exceeding available data storage. Conclusions Workflows that quantify gene expression are not new, and many already address issues of portability, reusability, and scale in terms of access to CPUs. GEMmaker provides these benefits and adds the ability to scale despite low data storage infrastructure. This allows users to process hundreds to thousands of RNA-seq samples even when data storage resources are limited. GEMmaker is freely available and fully documented with step-by-step setup and execution instructions. 

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5. Practical selection of representative sets of RNA-seq samples using a hierarchical approach

   https://doi.org/10.1093/bioinformatics/btab315  

   Tung, Laura H ; Kingsford, Carl ( July 2021 , Bioinformatics)

   Abstract Motivation Despite numerous RNA-seq samples available at large databases, most RNA-seq analysis tools are evaluated on a limited number of RNA-seq samples. This drives a need for methods to select a representative subset from all available RNA-seq samples to facilitate comprehensive, unbiased evaluation of bioinformatics tools. In sequence-based approaches for representative set selection (e.g. a k-mer counting approach that selects a subset based on k-mer similarities between RNA-seq samples), because of the large numbers of available RNA-seq samples and of k-mers/sequences in each sample, computing the full similarity matrix using k-mers/sequences for the entire set of RNA-seq samples in a large database (e.g. the SRA) has memory and runtime challenges; this makes direct representative set selection infeasible with limited computing resources. Results We developed a novel computational method called ‘hierarchical representative set selection’ to handle this challenge. Hierarchical representative set selection is a divide-and-conquer-like algorithm that breaks representative set selection into sub-selections and hierarchically selects representative samples through multiple levels. We demonstrate that hierarchical representative set selection can achieve summarization quality close to that of direct representative set selection, while largely reducing runtime and memory requirements of computing the full similarity matrix (up to 8.4× runtime reduction and 5.35× memory reduction for 10 000 and 12 000 samples respectively that could be practically run with direct subset selection). We show that hierarchical representative set selection substantially outperforms random sampling on the entire SRA set of RNA-seq samples, making it a practical solution to representative set selection on large databases like the SRA. Availability and implementation The code is available at https://github.com/Kingsford-Group/hierrepsetselection and https://github.com/Kingsford-Group/jellyfishsim. Supplementary information Supplementary data are available at Bioinformatics online. 

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