SAMtools Research Articles

MtDNA-Server 2: advancing mitochondrial DNA analysis through highly parallelized data processing and interactive analytics.

Over the past decade, mtDNA-Server established itself as one of the most widely used variant calling web-services for human mitochondrial genomes. The service accepts sequencing data in BAM format and returns an annotated variant analysis report for both homoplasmic and heteroplasmic variants. In this work we present mtDNA-Server 2, which includes several new features highly requested by the community. Most importantly, it includes (a) the integration of a novel variant calling mode that accurately call insertions, deletions and single nucleotide variants at once, (b) the integration of additional quality control and input validation modules, (c) a method to estimate the required coverage to minimize false positives and (d) an interactive analytics dashboard. Furthermore, we migrated the complete analysis workflow to the Nextflow workflow manager for improved parallelization, reproducibility and local execution. Recognizing the importance of insertions and deletions as well as offering novel quality control, validation and reporting features, mtDNA-Server 2 provides researchers and clinicians a new state-of-the-art analysis platform for interpreting mitochondrial genomes. mtDNA-Server 2 is available via mitoverse, our analysis platform that offers a centralized place for mtDNA analysis in the cloud. The web-service, source code and its documentation are freely accessible at https://mitoverse.i-med.ac.at.

Technical-economic feasibility of installing a photovoltaic generation plant: a case study in a public university

The energy compensation system has attracted the interest of several sectors, including the public sector. In this context, this work presents a technical dimensioning and techno-economic feasibility of installing a photovoltaic generation plant, considering the service to a Brazilian public university campus. Four systems were designed using NREL's PVWatts and SAM tools with the NSRDB database, analyzing 6 economic viability situations. The results show a payback of 6 years and 3 months for a 545kWp system without energy storage and 10 years and 5 months for a 180kWp system with energy storage. The variations in the results depend on the implementation considerations of each of the proposed systems, such as the energy availability demand contract and the savings on the bill made possible by the implementation method.

Techno-economic feasibility of installing a photovoltaic power plant: a case study at a public university

The energy compensation system has attracted the interest of several sectors, including the public sector. In this context, this work presents a technical dimensioning and techno-economic feasibility of installing a photovoltaic generation plant, considering the service to a Brazilian public university campus. Four systems were designed using NREL's PVWatts and SAM tools with the NSRDB database, analyzing 6 economic viability situations. The results show a payback of 6 years and 3 months for a 545kWp system without energy storage and 10 years and 5 months for a 180kWp system with energy storage. The variations in the results depend on the implementation considerations of each of the proposed systems, such as the energy availability demand contract and the savings on the bill made possible by the implementation method.

GASOLINE: detecting germline and somatic structural variants from long-reads data

Long-read sequencing allows analyses of single nucleic-acid molecules and produces sequences in the order of tens to hundreds kilobases. Its application to whole-genome analyses allows identification of complex genomic structural-variants (SVs) with unprecedented resolution. SV identification, however, requires complex computational methods, based on either read-depth or intra- and inter-alignment signatures approaches, which are limited by size or type of SVs. Moreover, most currently available tools only detect germline variants, thus requiring separate computation of sample pairs for comparative analyses. To overcome these limits, we developed a novel tool (Germline And SOmatic structuraL varIants detectioN and gEnotyping; GASOLINE) that groups SV signatures using a sophisticated clustering procedure based on a modified reciprocal overlap criterion, and is designed to identify germline SVs, from single samples, and somatic SVs from paired test and control samples. GASOLINE is a collection of Perl, R and Fortran codes, it analyzes aligned data in BAM format and produces VCF files with statistically significant somatic SVs. Germline or somatic analysis of 30×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document} sequencing coverage experiments requires 4–5 h with 20 threads. GASOLINE outperformed currently available methods in the detection of both germline and somatic SVs in synthetic and real long-reads datasets. Notably, when applied on a pair of metastatic melanoma and matched-normal sample, GASOLINE identified five genuine somatic SVs that were missed using five different sequencing technologies and state-of-the art SV calling approaches. Thus, GASOLINE identifies germline and somatic SVs with unprecedented accuracy and resolution, outperforming currently available state-of-the-art WGS long-reads computational methods.

Sequence Alignment/Map format: a comprehensive review of approaches and applications.

The Sequence Alignment/Map (SAM) format file is the text file used to record alignment information. Alignment is the core of sequencing analysis, and downstream tasks accept mapping results for further processing. Given the rapid development of the sequencing industry today, a comprehensive understanding of the SAM format and related tools is necessary to meet the challenges of data processing and analysis. This paper is devoted to retrieving knowledge in the broad field of SAM. First, the format of SAM is introduced to understand the overall process of the sequencing analysis. Then, existing work is systematically classified in accordance with generation, compression and application, and the involved SAM tools are specifically mined. Lastly, a summary and some thoughts on future directions are provided.

A quality control portal for sequencing data deposited at the European genome-phenome archive.

Since its launch in 2008, the European Genome–Phenome Archive (EGA) has been leading the archiving and distribution of human identifiable genomic data. In this regard, one of the community concerns is the potential usability of the stored data, as of now, data submitters are not mandated to perform any quality control (QC) before uploading their data and associated metadata information. Here, we present a new File QC Portal developed at EGA, along with QC reports performed and created for 1 694 442 files [Fastq, sequence alignment map (SAM)/binary alignment map (BAM)/CRAM and variant call format (VCF)] submitted at EGA. QC reports allow anonymous EGA users to view summary-level information regarding the files within a specific dataset, such as quality of reads, alignment quality, number and type of variants and other features. Researchers benefit from being able to assess the quality of data prior to the data access decision and thereby, increasing the reusability of data (https://ega-archive.org/blog/data-upcycling-powered-by-ega/).

Integrative Comparison of Burrows-Wheeler Transform-Based Mapping Algorithm with de Bruijn Graph for Identification of Lung/Liver Cancer-Specific Gene.

Cancers of the lung and liver are the top 10 leading causes of cancer death worldwide. Thus, it is essential to identify the genes specifically expressed in these two cancer types to develop new therapeutics. Although many messenger RNA (mRNA) sequencing data related to these cancer cells are available due to the advancement of next-generation sequencing (NGS) technologies, optimized data processing methods need to be developed to identify the novel cancer-specific genes. Here, we conducted an analytical comparison between Bowtie2, a Burrows-Wheeler transform-based alignment tool, and Kallisto, which adopts pseudo alignment based on a transcriptome de Bruijn graph using mRNA sequencing data on normal cells and lung/liver cancer tissues. Before using cancer data, simulated mRNA sequencing reads were generated, and the high Transcripts Per Million (TPM) values were compared. mRNA sequencing reads data on lung/liver cancer cells were also extracted and quantified. While Kallisto could directly give the output in TPM values, Bowtie2 provided the counts. Thus, TPM values were calculated by processing the Sequence Alignment Map (SAM) file in R using package Rsubread and subsequently in python. The analysis of the simulated sequencing data revealed that Kallisto could detect more transcripts and had a higher overlap over Bowtie2. The evaluation of these two data processing methods using the known lung cancer biomarkers concludes that in standard settings without any dedicated quality control, Kallisto is more effective at producing faster and more accurate results than Bowtie2. Such conclusions were also drawn and confirmed with the known biomarkers specific to liver cancer.

Whole Exome Sequencing Data Analysis for Detection of Breast Cancer Gene Variants and Pathway Study

Introduction: Whole Exome Sequencing (WES) involves sequencing, analysis of protein coding regions in genome. In present investigation, the potential gene variants were identified in human breast cancer genome using WES data analysis. Materials and Method: The NGS data samples with accession numbers (SRR1274896_1, SRR1274896_2) and (SRR1275000_1, SRR1275000_2) were collected from ENA database. The quality of the samples was assessed by using FastQC tool and followed by aligning samples with reference genome sequence hg38 using Bowtie2 tool. The results were retrieved in SAM format and converted to BAM format and then to sorted bam file using SAM tools, then duplicates were removed using Picard tool. Finally, Variant Calling format file was generated using BCF tools which projected the possible gene variants in the samples. Results: The results showed variant types out of them MUC3A1 showed an average of 53 mutations, highlighting its importance as a potential gene variant observed in breast cancer. Out of nonsynonymous mutations of samples, common gene variants in samples that possess 5 and more mutations were selected. The study was carried out on pathway analysis, domain analysis, gene involvement in biological processes and gene function. Conclusion: Majority of gene variants were involved in DNA Biosynthesis and Protein Biosynthesis and also resulted in tissue specific location. The location of these genes showed mutated genes in cytoplasm and in nucleus indicating the impact of gene variation on intracellular process.

O-087 Embryos with higher mitochondrial DNA ratios show better clinical outcomes in single euploid embryo transfer

Abstract Study question To assess whether there is a relationship between mitochondrial DNA content and implantation result. Summary answer The embryos with a higher mitochondrial DNA ratio increase pregnancy rate and implantation rate in single euploid embryo transfer. What is known already Mitochondria is an important organelle that generates energy during embryonic development. Recent literature points out that mitochondrial content and function may be related to implantation success and embryo viability. Some studies have linked increased ratios of mitochondrial DNA to aneuploidy, advanced maternal age, and euploid blastocyst with implantation failure, while others have failed to demonstrate similar findings. Study design, size, duration This study is a retrospective cohort study from 2016 to 2019, including 1465 single embryo transfer cycles. Participants/materials, setting, methods The involved embryos were biopsied on Day 5 or 6 and the mitochondrial DNA ratio of 1465 embryos was examined undergoing PGS/NGS. The mitochondrial DNA ratios were normalized for technical batch-to-batch variation. The mitochondrial DNA ratio between the implantation group and non-implantation group was statistically analyzed. Data were analyzed by the student’s t-test for continuous variables and Chi-square test for categorical variables. Main results and the role of chance The mitochondrial DNA ratio of embryos was no significant difference between different age spans ( p = 0.772) and ploidy (p = 0.224). D5 biopsied embryos, however, contained a significantly higher mitochondrial DNA ratio than D6 biopsied embryos (p < 0.0001). All of the single embryo transferred embryos were classified into two groups; implanted and non-implanted embryos. Results from 1465 transferred embryos show that the mitochondrial DNA ratio of implanted embryos was statistically significantly higher than non-implanted embryos (p = 0.0053). Besides, the cut-off values were established, dividing the transferred embryos into high and low mitochondrial DNA ratio groups. The pregnancy rate and implantation rate of the high mitochondrial DNA ratio group was higher than the low mitochondrial DNA ratio group: [Pregnancy rate] 74% vs. 63.5% (p = 0.0209); [Implantation rate] 57.3% vs. 50.8% (p = 0.1907). Limitations, reasons for caution The mitochondrial DNA ratios were analyzed by bioinformatics processing in Miseq reporter software (Illumina) files in the BAM and FASTQ format. Not sure if there is reproducibility in different sequencing platforms. Wider implications of the findings There still remains a lack of clarity regarding the relationship between mitochondrial function and transfer outcome. This retrospective study links an association between increased mtDNA content and increased implantation. Trial registration number not applicable

Pheniqs 2.0: accurate, high-performance Bayesian decoding and confidence estimation for combinatorial barcode indexing

BackgroundSystems biology increasingly relies on deep sequencing with combinatorial index tags to associate biological sequences with their sample, cell, or molecule of origin. Accurate data interpretation depends on the ability to classify sequences based on correct decoding of these combinatorial barcodes. The probability of correct decoding is influenced by both sequence quality and the number and arrangement of barcodes. The rising complexity of experimental designs calls for a probability model that accounts for both sequencing errors and random noise, generalizes to multiple combinatorial tags, and can handle any barcoding scheme. The needs for reproducibility and community benchmark standards demand a peer-reviewed tool that preserves decoding quality scores and provides tunable control over classification confidence that balances precision and recall. Moreover, continuous improvements in sequencing throughput require a fast, parallelized and scalable implementation.Results and discussionWe developed a flexible, robustly engineered software that performs probabilistic decoding and supports arbitrarily complex barcoding designs. Pheniqs computes the full posterior decoding error probability of observed barcodes by consulting basecalling quality scores and prior distributions, and reports sequences and confidence scores in Sequence Alignment/Map (SAM) fields. The product of posteriors for multiple independent barcodes provides an overall confidence score for each read. Pheniqs achieves greater accuracy than minimum edit distance or simple maximum likelihood estimation, and it scales linearly with core count to enable the classification of > 11 billion reads in 1 h 15 m using < 50 megabytes of memory. Pheniqs has been in production use for seven years in our genomics core facility.ConclusionWe introduce a computationally efficient software that implements both probabilistic and minimum distance decoders and show that decoding barcodes using posterior probabilities is more accurate than available methods. Pheniqs allows fine-tuning of decoding sensitivity using intuitive confidence thresholds and is extensible with alternative decoders and new error models. Any arbitrary arrangement of barcodes is easily configured, enabling computation of combinatorial confidence scores for any barcoding strategy. An optimized multithreaded implementation assures that Pheniqs is faster and scales better with complex barcode sets than existing tools. Support for POSIX streams and multiple sequencing formats enables easy integration with automated analysis pipelines.

SomatoSim: precision simulation of somatic single nucleotide variants

BackgroundSomatic single nucleotide variants have gained increased attention because of their role in cancer development and the widespread use of high-throughput sequencing techniques. The necessity to accurately identify these variants in sequencing data has led to a proliferation of somatic variant calling tools. Additionally, the use of simulated data to assess the performance of these tools has become common practice, as there is no gold standard dataset for benchmarking performance. However, many existing somatic variant simulation tools are limited because they rely on generating entirely synthetic reads derived from a reference genome or because they do not allow for the precise customizability that would enable a more focused understanding of single nucleotide variant calling performance.ResultsSomatoSim is a tool that lets users simulate somatic single nucleotide variants in sequence alignment map (SAM/BAM) files with full control of the specific variant positions, number of variants, variant allele fractions, depth of coverage, read quality, and base quality, among other parameters. SomatoSim accomplishes this through a three-stage process: variant selection, where candidate positions are selected for simulation, variant simulation, where reads are selected and mutated, and variant evaluation, where SomatoSim summarizes the simulation results.ConclusionsSomatoSim is a user-friendly tool that offers a high level of customizability for simulating somatic single nucleotide variants. SomatoSim is available at https://github.com/BieseckerLab/SomatoSim.

Transcriptome based high-throughput SSRs and SNPs discovery in the medicinal plant Lagenaria siceraria

<i>Lagenaria siceraria</i> (Molina) Standley has unique biological characteristics with high nutritional and medicinal values. It is an important pharmaceutical plant with various biologically active ingredients. Genetic improvement and deeper genomic studies require a rich resource of molecular markers. The application of next-generation sequencing technology, especially for transcriptome profiling, has greatly facilitated high throughput single nucleotide polymorphism (SNP) and simple sequence repeat (SSR) discovery. In this study, we sequenced the transcriptome of three major cultivars of <i>L. siceraria</i> and obtained 64.88 GB of clean data. The assembled high-quality reads were clustered into 89,347 unigenes, which were annotated by non-redundant protein database, Swiss-Port, Eukaryotic Ortholog Groups, Kyoto Encyclopedia of Genes and Genomes, and gene ontology databases. A total of 8,891 SSR and 35,873 SNP markers were predicted from unigenes by MISA and SAM tools, respectively. Characterization of the predicted markers in <i>L. siceraria</i> showed that the SSR and SNP densities were 60 and 243 markers per Mb of genome, respectively, and the estimated ratio of transition to transversion of SNP was 2.016. These markers will be very useful for genetic studies in <i>L. siceraria</i>, especially for the high-density linkage map construction and genome-wide association studies. Further genomic studies based on these results will facilitate the identification of novel genes or alleles of pharmaceutical importance.

Multi-Granularity Sequence Alignment Mapping for Encoder-Decoder Based End-to-End ASR

Encoder-decoder based automatic speech recognition (ASR) methods are increasingly popular due to their simplified processing stages and low reliance on prior knowledge. Conventional encoder-decoder based approaches usually learn a sequence-to-sequence mapping function from the source speech to target units (e.g., subwords, characters) in an end-to-end manner. However, it is still unclear how to choose the optimal target unit, or granularity of multiple units. In general, as increasing the information available for learning sequence-to-sequence mapping functions can improve modeling effectiveness, we therefore propose a multi-granularity sequence alignment (MGSA) approach. This aims to enhance cross-sequence interactions between different granularity units for both modeling and inference stages in the encoder-decoder based ASR. Specifically, a decoder module is designed to generate multi-granularity sequence predictions. We then exploit the latent alignment mapping among units having different levels of granularity, by utilizing the decoded multi-level sequences as input for model prediction. The cross-sequence interaction can also be employed to re-calibrate output probabilities in the proposed post-inference algorithm. Experimental results on both WSJ-80 hrs and Switchboard-300 hrs datasets show the superiority of the proposed method compared to traditional multi-task methods as well as to single granularity baseline systems.

Recalibration of mapping quality scores in Illumina short-read alignments improves SNP detection results in low-coverage sequencing data.

BackgroundLow-coverage sequencing is a cost-effective way to obtain reads spanning an entire genome. However, read depth at each locus is low, making sequencing error difficult to separate from actual variation. Prior to variant calling, sequencer reads are aligned to a reference genome, with alignments stored in Sequence Alignment/Map (SAM) files. Each alignment has a mapping quality (MAPQ) score indicating the probability a read is incorrectly aligned. This study investigated the recalibration of probability estimates used to compute MAPQ scores for improving variant calling performance in single-sample, low-coverage settings.Materials and MethodsSimulated tomato, hot pepper and rice genomes were implanted with known variants. From these, simulated paired-end reads were generated at low coverage and aligned to the original reference genomes. Features extracted from the SAM formatted alignment files for tomato were used to train machine learning models to detect incorrectly aligned reads and output estimates of the probability of misalignment for each read in all three data sets. MAPQ scores were then re-computed from these estimates. Next, the SAM files were updated with new MAPQ scores. Finally, Variant calling was performed on the original and recalibrated alignments and the results compared.ResultsIncorrectly aligned reads comprised only 0.16% of the reads in the training set. This severe class imbalance required special consideration for model training. The F1 score for detecting misaligned reads ranged from 0.76 to 0.82. The best performing model was used to compute new MAPQ scores. Single Nucleotide Polymorphism (SNP) detection was improved after mapping score recalibration. In rice, recall for called SNPs increased by 5.2%, while for tomato and pepper it increased by 3.1% and 1.5%, respectively. For all three data sets the precision of SNP calls ranged from 0.91 to 0.95, and was largely unchanged both before and after mapping score recalibration.ConclusionRecalibrating MAPQ scores delivers modest improvements in single-sample variant calling results. Some variant callers operate on multiple samples simultaneously. They exploit every sample’s reads to compensate for the low read-depth of individual samples. This improves polymorphism detection and genotype inference. It may be that small improvements in single-sample settings translate to larger gains in a multi-sample experiment. A study to investigate this is ongoing.

Optimizing performance of GATK workflows using Apache Arrow In-Memory data framework

BackgroundImmense improvements in sequencing technologies enable producing large amounts of high throughput and cost effective next-generation sequencing (NGS) data. This data needs to be processed efficiently for further downstream analyses. Computing systems need this large amounts of data closer to the processor (with low latency) for fast and efficient processing. However, existing workflows depend heavily on disk storage and access, to process this data incurs huge disk I/O overheads. Previously, due to the cost, volatility and other physical constraints of DRAM memory, it was not feasible to place large amounts of working data sets in memory. However, recent developments in storage-class memory and non-volatile memory technologies have enabled computing systems to place huge data in memory to process it directly from memory to avoid disk I/O bottlenecks. To exploit the benefits of such memory systems efficiently, proper formatted data placement in memory and its high throughput access is necessary by avoiding (de)-serialization and copy overheads in between processes. For this purpose, we use the newly developed Apache Arrow, a cross-language development framework that provides language-independent columnar in-memory data format for efficient in-memory big data analytics. This allows genomics applications developed in different programming languages to communicate in-memory without having to access disk storage and avoiding (de)-serialization and copy overheads.ImplementationWe integrate Apache Arrow in-memory based Sequence Alignment/Map (SAM) format and its shared memory objects store library in widely used genomics high throughput data processing applications like BWA-MEM, Picard and GATK to allow in-memory communication between these applications. In addition, this also allows us to exploit the cache locality of tabular data and parallel processing capabilities through shared memory objects.ResultsOur implementation shows that adopting in-memory SAM representation in genomics high throughput data processing applications results in better system resource utilization, low number of memory accesses due to high cache locality exploitation and parallel scalability due to shared memory objects. Our implementation focuses on the GATK best practices recommended workflows for germline analysis on whole genome sequencing (WGS) and whole exome sequencing (WES) data sets. We compare a number of existing in-memory data placing and sharing techniques like ramDisk and Unix pipes to show how columnar in-memory data representation outperforms both. We achieve a speedup of 4.85x and 4.76x for WGS and WES data, respectively, in overall execution time of variant calling workflows. Similarly, a speedup of 1.45x and 1.27x for these data sets, respectively, is achieved, as compared to the second fastest workflow. In some individual tools, particularly in sorting, duplicates removal and base quality score recalibration the speedup is even more promising.AvailabilityThe code and scripts used in our experiments are available in both container and repository form at: https://github.com/abs-tudelft/ArrowSAM.

IonCRAM: a reference-based compression tool for ion torrent sequence files

BackgroundIon Torrent is one of the major next generation sequencing (NGS) technologies and it is frequently used in medical research and diagnosis. The built-in software for the Ion Torrent sequencing machines delivers the sequencing results in the BAM format. In addition to the usual SAM/BAM fields, the Ion Torrent BAM file includes technology-specific flow signal data. The flow signals occupy a big portion of the BAM file (about 75% for the human genome). Compressing SAM/BAM into CRAM format significantly reduces the space needed to store the NGS results. However, the tools for generating the CRAM formats are not designed to handle the flow signals. This missing feature has motivated us to develop a new program to improve the compression of the Ion Torrent files for long term archiving.ResultsIn this paper, we present IonCRAM, the first reference-based compression tool to compress Ion Torrent BAM files for long term archiving. For the BAM files, IonCRAM could achieve a space saving of about 43%. This space saving is superior to what achieved with the CRAM format by about 8–9%.ConclusionsReducing the space consumption of NGS data reduces the cost of storage and data transfer. Therefore, developing efficient compression software for clinical NGS data goes beyond the computational interest; as it ultimately contributes to the overall cost reduction of the clinical test. The space saving achieved by our tool is a practical step in this direction. The tool is open source and available at Code Ocean, github, and http://ioncram.saudigenomeproject.com.

CONCUR: quick and robust calculation of codon usage from ribosome profiling data.

SummaryCONCUR is a standalone tool for codon usage analysis in ribosome profiling experiments. CONCUR uses the aligned reads in BAM format to estimate codon counts at the ribosome E-, P- and A-sites and at flanking positions.Availability and implementationCONCUR is written in Perl and is freely available at https://github.com/susbo/concur.Supplementary information Supplementary data are available at Bioinformatics online.

Identifying suitable tools for variant detection and differential gene expression using RNA-seq data

Neurodegenerative diseases are the most predominate brain disorders around the globe and the affected populations are rapidly increasing. Recently, these diseases have been addressed using the data obtained from RNA-sequencing technology to reveal the changes in gene/transcript expression, effect of variants, and pathways involved in disease mechanisms. However, the observations mainly depend on the aligners/tools and the performance of existing RNA-seq tools on hg38 genome assembly has not yet been documented. In this study, we performed a systematic analysis of various spliced aligners, transcript assembling and variant calling tools based on both genomic assemblies (hg19/hg38) from hippocampus brain tissue. This helps to identify the best possible combination tools for hg38 annotation. In order to evaluate the identified variants from various pipelines, we compared them with expression Quantitative Trait Loci (eQTL) and Genome-Wide Association Study (GWAS). In addition, the identified differentially expressed genes (DG) were compared with microarray studies. From our analysis of variant calling, the combination of GATK (Genome Analysis Tool-kit) and STAR (Spliced Transcripts Alignment to a Reference) protocol yields a larger number of GWAS/eQTL variants compared to SAMtools (Sequence Alignment Map). We also identified a higher number of non-coding variants in hg38 compared to hg19 due to enhanced annotation. In the case of various DG pipelines, we found that the Salmon-based hg38 transcriptomic quantification yields a higher number of reported DG compared to other genome-based quantification methods. This study revealed that higher number of reads maps to multiple location of the genome with hg38 compared to hg19, and these spurious multi-mapped reads may affect the gene quantification techniques. We suggest that it is necessary to develop efficient algorithms, which can handle the multi-mapped reads and improve the performance of genome-based alignment quantification.

Novel bioinformatics quality control metric for next-generation sequencing experiments in the clinical context.

As the use of next-generation sequencing (NGS) for the Mendelian diseases diagnosis is expanding, the performance of this method has to be improved in order to achieve higher quality. Typically, performance measures are considered to be designed in the context of each application and, therefore, account for a spectrum of clinically relevant variants. We present EphaGen, a new computational methodology for bioinformatics quality control (QC). Given a single NGS dataset in BAM format and a pre-compiled VCF-file of targeted clinically relevant variants it associates this dataset with a single arbiter parameter. Intrinsically, EphaGen estimates the probability to miss any variant from the defined spectrum within a particular NGS dataset. Such performance measure virtually resembles the diagnostic sensitivity of given NGS dataset. Here we present case studies of the use of EphaGen in context of BRCA1/2 and CFTR sequencing in a series of 14 runs across 43 blood samples and 504 publically available NGS datasets. EphaGen is superior to conventional bioinformatics metrics such as coverage depth and coverage uniformity. We recommend using this software as a QC step in NGS studies in the clinical context. Availability: https://github.com/m4merg/EphaGen or https://hub.docker.com/r/m4merg/ephagen.

Bazam: a rapid method for read extraction and realignment of high-throughput sequencing data

The vast quantities of short-read sequencing data being generated are often exchanged and stored as aligned reads. However, aligned data becomes outdated as new reference genomes and alignment methods become available. Here we describe Bazam, a tool that efficiently extracts the original paired FASTQ from alignment files (BAM or CRAM format) in a format that directly allows efficient realignment. Bazam facilitates up to a 90% reduction in the time for realignment compared to standard methods. Bazam can support selective extraction of read pairs from focused genomic regions for applications such as targeted region analyses, quality control, structural variant calling, and alignment comparisons.