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

The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

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

Gene structural annotation is a critical step in obtaining biological knowledge from genome sequences yet remains a major challenge in genomics projects. Current de novo Hidden Markov Models are limited in their capacity to model biological complexity; while current pipelines are resource-intensive and their results vary in quality with the available extrinsic data. Here, we build on our previous work in applying Deep Learning to gene calling to make a fully applicable, fast and user friendly tool for predicting primary gene models from DNA sequence alone. The quality is state-of-the-art, with predictions scoring closer by most measures to the references than to predictions from other de novo tools. Helixer’s predictions can be used as is or could be integrated in pipelines to boost quality further. Moreover, there is substantial potential for further improvements and advancements in gene calling with Deep Learning. Helixer is open source and available at https://github.com/weberlab-hhu/Helixer A web interface is available at https://www.plabipd.de/helixer_main.html

/pdf/helixer-de-novo-prediction-of-primary-eukaryotic-gene-models-21jmlfa1.pdf

Helixer–de novo Prediction of Primary Eukaryotic Gene Models Combining Deep Learning and a Hidden Markov Model

Recent massive growth in the production of sequencing data necessitates matching improvements in bioinformatics tools to effectively utilize it. Existing tools suffer from limitations in both scalability and applicability which are inherent to their underlying algorithms and data structures. We identify the key requirements for the ideal data structure for sequence analyses: it should be informationally lossless, locally updatable, and memory efficient; requirements which are not met by data structures underlying the major assembly strategies Overlap Layout Consensus and De Bruijn Graphs. We therefore propose a new data structure, the LOGAN graph, which is based on a memory efficient Sparse De Bruijn Graph with routing information. Innovations in storing routing information and careful implementation allow sequence datasets for Escherichia coli (4.6Mbp, 117x coverage), Arabidopsis thaliana (135Mbp, 17.5x coverage) and Solanum pennellii (1.2Gbp, 47x coverage) to be loaded into memory on a desktop computer in seconds, minutes, and hours respectively. Memory consumption is competitive with state of the art alternatives, while losslessly representing the reads in an indexed and updatable form. Both Second and Third Generation Sequencing reads are supported. Thus, the LOGAN graph is positioned to be the backbone for major breakthroughs in sequence analysis such as integrated hybrid assembly, assembly of exceptionally large and repetitive genomes, as well as assembly and representation of pan-genomes.

/pdf/logan-a-framework-for-lossless-graph-based-analysis-of-high-4ouj6g996d.pdf

LOGAN: A framework for LOssless Graph-based ANalysis of high throughput sequence data

A Solanaceous plant exhibiting a facultative parthenocarpy is provided. The plant comprises a loss-of-function mutation in a Sl AGL6 gene and alternatively or additionally characterized by an average fruit weight/plant at least about the same as that of a non-parthenocarpic tomato of the same genetic background under fertilization permissive conditions of the non-parthenocarpic tomato. Also provided are methods of producing such plants and processed products produced from same.

Parthenocarpic plants and methods of producing same

&lt;p&gt;&lt;b&gt;Play is a physical act of discovery and exploration. Learning through the act of play for children is well documented and understood. Whether play is experienced physically or cognitively, there are clear pedagogical benefits to play in education.&lt;/b&gt;&lt;/p&gt; &lt;p&gt;When architecture catalyses interaction unrestricted by formal social etiquette, children are constantly engaged with improvised play in the schooling environment.&lt;/p&gt; &lt;p&gt;The traditional horizontal model of schooling supports this act of unscripted play; however, in the transition to the vertical school model, these spaces are easily lost as efficiency is privileged. As seen in overseas examples, the high- rise school typology development typically centres on optimising space circulation. In response, play space is typically relegated to a green roof, a sports court, or perhaps a formal play area.&lt;/p&gt; &lt;p&gt;And yet, schools play a critical role in developing the creativity and expression of the youth in our community. With the population inWellingtonCentralexpectedto grow by 11,700 people in 2030 and the WCC plan is to develop a new primary school, vertical schools are a viable option for the future. However, there is a risk that the qualities of these spaces will be more reflective of the corporate office than a school.&lt;/p&gt; &lt;p&gt;This thesis aims to develop a vertical primary school building model within Wellington City that prioritises play.&lt;/p&gt; &lt;p&gt;Thus, the research statement becomes; Using a vertical school typology and employing play theory this creative body of work explores how the design of a vertical primary school in wellington can be driven through the lens of play.&lt;/p&gt; 

Ludic Provocation

Plant immunity relies on the perception of immunogenic signals by cell-surface and intracellular receptors and subsequent activation of defense responses like programmed cell death. Under certain circumstances, the fine-tuned innate immune system of plants results in the activation of autoimmune responses that cause constitutive defense responses and spontaneous cell death in the absence of pathogens.Here, we characterized the onset of leaf death 12 (old12) mutant that was identified in the Arabidopsis accession Landsberg erecta. The old12 mutant is characterized by a growth defect, spontaneous cell death, plant-defense gene activation, and early senescence. In addition, the old12 phenotype is temperature reversible, thereby exhibiting all characteristics of an autoimmune mutant. Mapping the mutated locus revealed that the old12 phenotype is caused by a mutation in the Lectin Receptor Kinase P2-TYPE PURINERGIC RECEPTOR 2 (P2K2) gene. Interestingly, the P2K2 allele from Landsberg erecta is conserved among Brassicaceae. P2K2 has been implicated in pathogen tolerance and sensing extracellular ATP. The constitutive activation of defense responses in old12 results in improved resistance against Pseudomonas syringae pv. tomato DC3000.We demonstrate that old12 is an auto-immune mutant and that allelic variation of P2K2 contributes to diversity in Arabidopsis immune responses. 

/pdf/the-arabidopsis-thaliana-onset-of-leaf-death-12-mutation-in-28h4qa8f.pdf

Anthony Bolger

Papers

Helixer–de novo Prediction of Primary Eukaryotic Gene Models Combining Deep Learning and a Hidden Markov Model

LOGAN: A framework for LOssless Graph-based ANalysis of high throughput sequence data

Parthenocarpic plants and methods of producing same

Ludic Provocation

The Arabidopsis thaliana onset of leaf death 12 mutation in the lectin receptor kinase P2K2 results in an autoimmune phenotype