Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an opensource software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de

/pdf/htseq-a-python-framework-to-work-with-high-throughput-471c89qgd6.pdf

HTSeq—a Python framework to work with high-throughput sequencing data

Emerging infectious diseases, such as severe acute respiratory syndrome (SARS) and Zika virus disease, present a major threat to public health1–3. Despite intense research efforts, how, when and where new diseases appear are still a source of considerable uncertainty. A severe respiratory disease was recently reported in Wuhan, Hubei province, China. As of 25 January 2020, at least 1,975 cases had been reported since the first patient was hospitalized on 12 December 2019. Epidemiological investigations have suggested that the outbreak was associated with a seafood market in Wuhan. Here we study a single patient who was a worker at the market and who was admitted to the Central Hospital of Wuhan on 26 December 2019 while experiencing a severe respiratory syndrome that included fever, dizziness and a cough. Metagenomic RNA sequencing4 of a sample of bronchoalveolar lavage fluid from the patient identified a new RNA virus strain from the family Coronaviridae, which is designated here ‘WH-Human 1’ coronavirus (and has also been referred to as ‘2019-nCoV’). Phylogenetic analysis of the complete viral genome (29,903 nucleotides) revealed that the virus was most closely related (89.1% nucleotide similarity) to a group of SARS-like coronaviruses (genus Betacoronavirus, subgenus Sarbecovirus) that had previously been found in bats in China5. This outbreak highlights the ongoing ability of viral spill-over from animals to cause severe disease in humans. Phylogenetic and metagenomic analyses of the complete viral genome of a new coronavirus from the family Coronaviridae reveal that the virus is closely related to a group of SARS-like coronaviruses found in bats in China.

/pdf/a-new-coronavirus-associated-with-human-respiratory-disease-4bxsuj7unz.pdf

A new coronavirus associated with human respiratory disease in China.

Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2-5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.

/pdf/fastp-an-ultra-fast-all-in-one-fastq-preprocessor-ehzxan9vkg.pdf

fastp: an ultra-fast all-in-one FASTQ preprocessor.

De novo assembly of RNA-seq data enables researchers to study transcriptomes without the need for a genome sequence; this approach can be usefully applied, for instance, in research on 'non-model organisms' of ecological and evolutionary importance, cancer samples or the microbiome. In this protocol we describe the use of the Trinity platform for de novo transcriptome assembly from RNA-seq data in non-model organisms. We also present Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes. In the procedure, we provide a workflow for genome-independent transcriptome analysis leveraging the Trinity platform. The software, documentation and demonstrations are freely available from http://trinityrnaseq.sourceforge.net. The run time of this protocol is highly dependent on the size and complexity of data to be analyzed. The example data set analyzed in the procedure detailed herein can be processed in less than 5 h.

http://genomebio.org/wp-content/uploads/2013/08/Haas_2013_Nature_Protocols-1.pdf

De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis

Motivation: Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming, and quality filtering. These tools are often insufficiently fast as most are developed using high level programming languages (e.g., Python and Java) and provide limited multithreading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results: We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per read quality cutting, and many other operations with a single scan of the FASTQ data. It also supports unique molecular identifier preprocessing, poly tail trimming, output splitting, and base correction for paired-end data. It can automatically detect adapters for single-end and paired-end FASTQ data. This tool is developed in C++ and has multithreading support. Based on our evaluation, fastp is 2 to 5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and Implementation: The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp

https://www.biorxiv.org/content/biorxiv/early/2018/03/01/274100.full.pdf

fastp: an ultra-fast all-in-one FASTQ preprocessor

Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data.

Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested.

Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic

Contact: ed.nehcaa-htwr.1oib@ledasu

Supplementary information: Supplementary data are available at Bioinformatics online.

Trimmomatic: a flexible trimmer for Illumina sequence data

Mitochondria and plastids (chloroplasts) are cell organelles of endosymbiotic origin that possess their own genetic information. Most organellar DNAs map as circular double-stranded genomes. Across the eukaryotic kingdom, organellar genomes display great size variation, ranging from ∼15 to 20 kb (the size of the mitochondrial genome in most animals) to >10 Mb (the size of the mitochondrial genome in some lineages of flowering plants). We have developed OrganellarGenomeDraw (OGDRAW), a suite of software tools that enable users to create high-quality visual representations of both circular and linear annotated genome sequences provided as GenBank files or accession numbers. Although all types of DNA sequences are accepted as input, the software has been specifically optimized to properly depict features of organellar genomes. A recent extension facilitates the plotting of quantitative gene expression data, such as transcript or protein abundance data, directly onto the genome map. OGDRAW has already become widely used and is available as a free web tool (http://ogdraw.mpimp-golm.mpg.de/). The core processing components can be downloaded as a Perl module, thus also allowing for convenient integration into custom processing pipelines.

/pdf/organellargenomedraw-a-suite-of-tools-for-generating-y32hi0wecj.pdf

OrganellarGenomeDRAW—a suite of tools for generating physical maps of plastid and mitochondrial genomes and visualizing expression data sets

Mitochondria and plastids are DNA-containing cell organelles whose genomes occur at high copy numbers per cell. Organellar genomes vary greatly in size ranging from approximately 15 kb for some animal mitochondrial genomes to more than 2 Mb for some plant mitochondrial genomes. The vast majority of organellar genomes map as circular molecules that are difficult to illustrate by available commercial or free software tools. Thus, published genome maps are extremely heterogeneous in design, often tediously drawn semi-manually and lack any consensus in display. Here, we present a new web-based tool, OrganellarGenomeDRAW (OGDRAW), which produces high-resolution custom graphical maps of DNA sequences as stored in standard GenBank format entries. GenBank data can be provided as either file uploads or accession numbers. The program is specially optimized for the display of chloroplast and mitochondrial genomes but can also be used to depict other circular DNA sequences. The design of the program core as a Perl module with an object-oriented interface allows easy integration into custom scripts.

OrganellarGenomeDRAW (OGDRAW): a tool for the easy generation of high-quality custom graphical maps of plastid and mitochondrial genomes.

Recent rapid advances in next generation RNA sequencing (RNA-Seq)-based provide researchers with unprecedentedly large data sets and open new perspectives in transcriptomics. Furthermore, RNA-Seq-based transcript profiling can be applied to non-model and newly discovered organisms because it does not require a predefined measuring platform (like e.g. microarrays). However, these novel technologies pose new challenges: the raw data need to be rigorously quality checked and filtered prior to analysis, and proper statistical methods have to be applied to extract biologically relevant information. Given the sheer volume of data, this is no trivial task and requires a combination of considerable technical resources along with bioinformatics expertise. To aid the individual researcher, we have developed RobiNA as an integrated solution that consolidates all steps of RNA-Seq-based differential gene-expression analysis in one user-friendly cross-platform application featuring a rich graphical user interface. RobiNA accepts raw FastQ files, SAM/BAM alignment files and counts tables as input. It supports quality checking, flexible filtering and statistical analysis of differential gene expression based on state-of-the art biostatistical methods developed in the R/Bioconductor projects. In-line help and a step-by-step manual guide users through the analysis. Installer packages for Mac OS X, Windows and Linux are available under the LGPL licence from http://mapman.gabipd.org/web/guest/ robin.

/pdf/robina-a-user-friendly-integrated-software-solution-for-rna-3k2rjegdln.pdf

RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics

The specificity of intracellular signaling and developmental patterning in biological systems relies on selective interactions between different proteins in specific cellular compartments. The identification of such protein-protein interactions is essential for unraveling complex signaling and regulatory networks. Recently, bimolecular fluorescence complementation (BiFC) has emerged as a powerful technique for the efficient detection of protein interactions in their native subcellular localization. Here we report significant technical advances in the methodology of plant BiFC. We describe a series of versatile BiFC vector sets that are fully compatible with previously generated vectors. The new vectors enable the generation of both C-terminal and N-terminal fusion proteins and carry optimized fluorescent protein genes that considerably improve the sensitivity of BiFC. Using these vectors, we describe a multicolor BiFC (mcBiFC) approach for the simultaneous visualization of multiple protein interactions in the same cell. Application to a protein interaction network acting in calcium-mediated signal transduction revealed the concurrent interaction of the protein kinase CIPK24 with the calcium sensors CBL1 and CBL10 at the plasma membrane and tonoplast, respectively. We have also visualized by mcBiFC the simultaneous formation of CBL1/CIPK1 and CBL9/CIPK1 protein complexes at the plasma membrane. Thus, mcBiFC provides a useful new tool for exploring complex regulatory networks in plants.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-313X.2008.03612.x

Marc Lohse

Papers

Trimmomatic: a flexible trimmer for Illumina sequence data

OrganellarGenomeDRAW—a suite of tools for generating physical maps of plastid and mitochondrial genomes and visualizing expression data sets

OrganellarGenomeDRAW (OGDRAW): a tool for the easy generation of high-quality custom graphical maps of plastid and mitochondrial genomes.

RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics

Multicolor bimolecular fluorescence complementation reveals simultaneous formation of alternative CBL/CIPK complexes in planta