Abstract Motivation: Modern sequencing instruments are able to generate at least hundreds of millions short reads of genomic data. Those huge volumes of data require effective means to store them, provide quick access to any record and enable fast decompression. Results: We present a specialized compression algorithm for genomic data in FASTQ format which dominates its competitor, G-SQZ, as is shown on a number of datasets from the 1000 Genomes Project (www.1000genomes.org). Availability: DSRC is freely available at http:/sun.aei.polsl.pl/dsrc. Contact: sebastian.deorowicz@polsl.pl Supplementary information: Supplementary data are available at Bioinformatics online.

Pipelines for the analysis of Next-Generation Sequencing (NGS) data are generally composed of a set of different publicly available software, configured together in order to map short reads of a genome and call variants. The fidelity of pipelines is variable. We have developed ArtificialFastqGenerator, which takes a reference genome sequence as input and outputs artificial paired-end FASTQ files containing Phred quality scores. Since these artificial FASTQs are derived from the reference genome, it provides a gold-standard for read-alignment and variant-calling, thereby enabling the performance of any NGS pipeline to be evaluated. The user can customise DNA template/read length, the modelling of coverage based on GC content, whether to use real Phred base quality scores taken from existing FASTQ files, and whether to simulate sequencing errors. Detailed coverage and error summary statistics are outputted. Here we describe ArtificialFastqGenerator and illustrate its implementation in evaluating a typical bespoke NGS analysis pipeline under different experimental conditions. ArtificialFastqGenerator was released in January 2012. Source code, example files and binaries are freely available under the terms of the GNU General Public License v3.0. from https://sourceforge.net/projects/artfastqgen/.

Abstract Summary The FASTQ+ format is designed for single-cell experiments. It extends various optional tags, including cell barcodes and unique molecular identifiers, to the sequence identifier and is fully compatible with the FASTQ format. In addition, PISA implements various utilities for processing sequences in the FASTQ format and alignments in the SAM/BAM/CRAM format from single-cell experiments, such as converting FASTQ format to FASTQ+, annotating alignments, PCR deduplication, feature counting and barcodes correction. The software is open-source and written in C language. Availability and implementation https://doi.org/10.5281/zenodo.7007056 or https://github.com/shiquan/PISA Supplementary information Supplementary data are available at Bioinformatics online.

<h4>Background</h4>Modern Next Generation- and Third Generation- Sequencing methods such as Illumina and PacBio Circular Consensus Sequencing platforms provide accurate sequencing data. Parallel developments in Deep Learning have enabled the application of Deep Neural Networks to variant calling, surpassing the accuracy of classical approaches in many settings. DeepVariant, arguably the most popular among such methods, transforms the problem of variant calling into one of image recognition where a Deep Neural Network analyzes sequencing data that is formatted as images, achieving high accuracy. In this paper, we explore an alternative approach to designing Deep Neural Networks for variant calling, where we use meticulously designed Deep Neural Network architectures and customized variant inference functions that account for the underlying nature of sequencing data instead of converting the problem to one of image recognition.<h4>Results</h4>Results from 27 whole-genome variant calling experiments spanning Illumina, PacBio and hybrid Illumina-PacBio settings suggest that our method allows vastly smaller Deep Neural Networks to outperform the Inception-v3 architecture used in DeepVariant for indel and substitution-type variant calls. For example, our method reduces the number of indel call errors by up to 18%, 55% and 65% for Illumina, PacBio and hybrid Illumina-PacBio variant calling respectively, compared to a similarly trained DeepVariant pipeline. In these cases, our models are between 7 and 14 times smaller.<h4>Conclusions</h4>We believe that the improved accuracy and problem-specific customization of our models will enable more accurate pipelines and further method development in the field. HELLO is available at https://github.com/anands-repo/hello.

Motivation Output from high throughput sequencing instruments often exceeds what is necessary to assay a single sample. To better utilize this capacity, multiple samples are independently tagged with a unique “barcode” sequence and are then pooled, or “multiplexed”, and sequenced together. Classifying, or “demultiplexing”, the reads involves decoding the barcode sequence. Although instruments estimate the probability of incorrectly calling each nucleobase, available demultiplexers do not consult those estimates or report classification error probabilities. Results We present Pheniqs, a fast and flexible sequence demultiplexer and quality analyzer. In addition to providing an efficient implementation of the widespread minimum distance decoder, Pheniqs introduces a novel Phred-adjusted maximum likelihood decoder that consults base calling quality scores and estimates the probability of a barcode decoding error. Setting an upper bound on the permissible error provides an intuitive way to control demultiplexing confidence and directly influence precision and recall. Pheniqs supports FASTQ and multiple Sequence Alignment/Map formats and uses auxiliary SAM tags to report both library classification and demultiplexing error probability. Evaluation on both real and semi-synthetic data indicates that Pheniqs is faster than existing demultiplexers, substantially when demultiplexing longer reads, and achieves greater accuracy by correctly reflecting quality measurements. Availability and Implementation Implemented in multithreaded C++ and available under the terms of the AGPL-3.0 license agreement at http://github.com/biosails/pheniqs. Manual and examples are available at http://biosails.github.io/pheniqs.

Abstract Next-generation sequencing data is accompanied by quality scores that quantify sequencing error. Inaccuracies in these quality scores propagate through all subsequent analyses; thus base quality score recalibration is a standard step in many next-generation sequencing workflows, resulting in improved variant calls. Current base quality score recalibration algorithms rely on the assumption that sequencing errors are already known; for human resequencing data, relatively complete variant databases facilitate this. However, because existing databases are still incomplete, recalibration is still inaccurate; and most organisms do not have variant databases, exacerbating inaccuracy for non-human data. To overcome these logical and practical problems, we introduce Lacer, which recalibrates base quality scores without assuming knowledge of correct and incorrect bases and without requiring knowledge of common variants. Lacer is the first logically sound, fully general, and truly accurate base recalibrator. Lacer enhances variant identification accuracy for resequencing data of human as well as other organisms (which are not accessible to current recalibrators), simultaneously improving and extending the benefits of base quality score recalibration to nearly all ongoing sequencing projects. Lacer is available at: https://github.com/swainechen/lacer .

The Phred quality score can measure the sequence quality, and quality scores are normally stored together with the nucleotide sequence in the widely accepted FASTQ format. For sequence raw reads with various FASTQ formats, the range of scores will depend on the technology and the base caller used. With the different technology, the range of scores is different. For learning the comparing and converting method of sequence Phred quality score, we do an experiment, and analyze the different between various FASTQ quality formats. Introduction A Phred quality score is a measure of the quality of the identification of the nucleobases generated by automated DNA sequencing [1]. It was originally developed for Phred base calling to help in the automation of DNA sequencing in the Human Genome Project. Phred quality scores are assigned to each nucleotide base call in automated sequencer traces [2]. FASTQ format is a text-based format for storing both a biological sequence and its corresponding quality scores. Both the sequence letter and quality score are each encoded with a single ASCII character for brevity. For sequence raw reads with various FASTQ formats, the range of scores will depend on the technology and the base caller used. In this paper, we study Phred quality score and FASTQ format. Then, we make a RNA sequence as the experiment resource and do an experiment to analyze the different between various FASTQ quality formats for learning the comparing and converting method of sequence Phred quality score. Phred Quality Score Phred quality scores have become widely accepted to characterize the quality of DNA sequences, and can be used to compare the efficacy of different sequencing methods. Perhaps the most important use of Phred quality scores is the automatic determination of accurate, quality-based consensus sequences. Phred quality scores are used for assessment of sequence quality, recognition and removal of low-quality sequence (end clipping), and determination of accurate consensus sequences. Originally, Phred quality scores were primarily used by the sequence assembly program Phrap. Phrap was routinely used in some of the largest sequencing projects in the Human Genome Sequencing Project and is currently one of the most widely used DNA sequence assembly programs in the biotech industry. Phrap uses Phred quality scores to determine highly accurate consensus sequences and to estimate the quality of the consensus sequences. Phrap also uses Phred quality scores to estimate whether discrepancies between two overlapping sequences are more likely to arise from random errors, or from different copies of a repeated sequence. Within the Human Genome Project, the most important use of Phred quality scores was for automatic determination of consensus sequences. Before Phred and Phrap, scientists had to carefully look at discrepancies between overlapping DNA fragments; often, this involved manual determination 6th International Conference on Management, Education, Information and Control (MEICI 2016) © 2016. The authors – Published by Atlantis Press 0260 of the highest-quality sequence, and manual editing of any errors. Phrap's use of Phred quality scores effectively automated finding the highest-quality consensus sequence; in most cases, this completely circumvents the need for any manual editing. As a result, the estimated error rate in assemblies that were created automatically with Phred and Phrap is typically substantially lower than the error rate of manually edited sequence. In 2009, many commonly used software packages make use of Phred quality scores, albeit to a different extent. Programs like Sequencher use quality scores for display, end clipping, and consensus determination; other programs like CodonCode Aligner also implement quality-based consensus methods. Phred quality scores Q are defined as a property which is logarithmically related to the base-calling error probabilities P [2]. -Q 10 P = 10 (1) Phred quality scores

FASTQ has emerged as a common file format for sharing sequencing read data combining both the sequence and an associated per base quality score, despite lacking any formal definition to date, and existing in at least three incompatible variants. This article defines the FASTQ format, covering the original Sanger standard, the Solexa/Illumina variants and conversion between them, based on publicly available information such as the MAQ documentation and conventions recently agreed by the Open Bioinformatics Foundation projects Biopython, BioPerl, BioRuby, BioJava and EMBOSS. Being an open access publication, it is hoped that this description, with the example files provided as Supplementary Data, will serve in future as a reference for this important file format.

I'm delighted to announce an open access publication in &lt;em&gt; Nucleic Acids Research &lt;/em&gt; describing the FASTQ file format based on the conventions agreed by the OBF projects: This will hopefully serve as a reference describing the original standard Sanger FASTQ, and the two variants from Solexa/Illumina, and how to inter-convert between them.

I'm delighted to announce an open access publication in &lt;em&gt; Nucleic Acids Research &lt;/em&gt; describing the FASTQ file format based on the conventions agreed by the OBF projects: This will hopefully serve as a reference describing the original standard Sanger FASTQ, and the two variants from Solexa/Illumina, and how to inter-convert between them.