MOTIVATION: Many pathogen identification and microbiome analysis tools have been developed in recent years, with Kraken 2 being one of the most popular. While tools downstream of Kraken 2 can assist in the interpretation of its outputs, a statistical framework to assess the likelihood that a taxon/organism is present in a single sample alongside an automated end-to-end analysis pipeline has not yet been fully implemented. RESULTS: Here, we introduce SPARKI, an R package that performs statistical analysis of Kraken 2 outputs and aids in the identification of pathogens present in next-generation sequencing samples. SPARKI adds to the field by bringing a probabilistic view to Kraken 2 data, serving as a discovery tool and complementing other methods such as KrakenTools, Bracken, and Pavian. AVAILABILITY AND IMPLEMENTATION: SPARKI code is available on GitHub at https://github.com/team113sanger/sparki. SPARKI is also part of an end-to-end pathogen identification pipeline, sparki-nf, which is available at https://github.com/team113sanger/sparki-nf. An additional pipeline for further exploration and validation of SPARKI results is also available at https://github.com/team113sanger/map-to-genome.

MOTIVATION: CRISPR/Cas9-based technology allows for the functional analysis of genetic variants at single nucleotide resolution whilst maintaining genomic context. This approach, known as saturation genome editing (SGE), a form of deep mutational scanning, systematically alters each position in a target region to explore its function. SGE experiments require the design and synthesis of oligonucleotide variant libraries which are introduced into the genome. This technology is applicable to diverse fields such as disease variant identification, drug development, structure-function studies, synthetic biology, evolutionary genetics and host-pathogen interactions. Here, we present the Variant Library Annotation Tool (VaLiAnT) which can be used to generate variant libraries from user-defined genomic coordinates and standard input files. The software can accommodate user-specified species, reference sequences and transcript annotations. RESULTS: Coordinates for a genomic range are provided by the user to retrieve a corresponding oligonucleotide reference sequence. A user-specified range within this sequence is then subject to systematic, nucleotide and/or amino acid saturating mutator functions. VaLiAnT provides a novel way to retrieve, mutate and annotate genomic sequences for oligonucleotide library generation. Specific features for SGE library generation can be employed. In addition, VaLiAnT is configurable, allowing for cDNA and prime editing saturation library generation, with other diverse applications possible. AVAILABILITY AND IMPLEMENTATION: VaLiAnT is a command line tool written in Python. Source code, testing data, example input and output files and executables are available (https://github.com/cancerit/VaLiAnT) in addition to a detailed user manual (https://github.com/cancerit/VaLiAnT/wiki). VaLiAnT is licensed under AGPLv3. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Identifying disease-causing variants from exome sequencing projects remains a challenging task that often requires bioinformatics expertise. Here we describe a user-friendly graphical application that allows medical professionals and bench biologists to prioritize and visualize genetic variants from human exome sequencing data. RESULTS: We have implemented VCF/Plotein, a graphical, fully interactive web application able to display exome sequencing data in VCF format. Gene and variant information is extracted from Ensembl. Cross-referencing with external databases and application-based gene and variant filtering have also been implemented. All data processing is done locally by the user's CPU to ensure the security of patient data. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at https://vcfplotein.liigh.unam.mx. Website implemented in JavaScript using the Vue.js framework, with all major browsers supported. Source code freely available for download at https://github.com/raulossio/VCF-plotein. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Project to Integrate Technical Communication Habits (PITCH) is being implemented in the Tagliatela College of Engineering at the University of New Haven across seven engineering and computer science undergraduate programs. PITCH develops written, oral and visual communication skills in students starting in the very first semester and continuing through all four years of each program. Communication instruments encompass technical memoranda, poster presentations, oral presentations, laboratory reports, proposals, and senior design reports, including the use of tables and graphics in each. Advice tables, annotated sample assignments and grading rubrics are being developed for each instrument to assist students in their work and facilitate consistency in instruction and assessment across multiple instructors teaching different course sections. Within each of the seven programs, specific courses that span all four years are targeted for implementation and assessment of technical communication skills. The different communication instruments are distributed across courses as appropriate, and the skills are developed at deeper and deeper levels as students progress through the years. A critical feature of the project is that technical communication skills are integrated into the content of regular engineering courses and are taught by regular engineering faculty.

Abstract Summary: A significant proportion of eukaryote genomes consist of transposable element (TE)-derived sequence. These elements are known to have the capacity to modulate gene function and genome evolution. We have developed RetroSeq for detecting non-reference TE insertions from Illumina paired-end whole-genome sequencing data. We evaluate RetroSeq on a human trio from the 1000 Genomes Project, showing that it produces highly accurate TE calls. Availabilty: RetroSeq is open-source and available from https://github.com/tk2/RetroSeq. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract Summary: We have developed Cake, a bioinformatics software pipeline that integrates four publicly available somatic variant-calling algorithms to identify single nucleotide variants with higher sensitivity and accuracy than any one algorithm alone. Cake can be run on a high-performance computer cluster or used as a stand-alone application. Availabilty: Cake is open-source and is available from http://cakesomatic.sourceforge.net/ Contact: [email protected] Supplementary Information: Supplementary data are available at Bioinformatics online.

Abstract Summary: We have developed an algorithm to detect copy number variants (CNVs) in homozygous organisms, such as inbred laboratory strains of mice, from short read sequence data. Our novel approach exploits the fact that inbred mice are homozygous at virtually every position in the genome to detect CNVs using a hidden Markov model (HMM). This HMM uses both the density of sequence reads mapped to the genome, and the rate of apparent heterozygous single nucleotide polymorphisms, to determine genomic copy number. We tested our algorithm on short read sequence data generated from re-sequencing chromosome 17 of the mouse strains A/J and CAST/EiJ with the Illumina platform. In total, we identified 118 copy number variants (43 for A/J and 75 for CAST/EiJ). We investigated the performance of our algorithm through comparison to CNVs previously identified by array-comparative genomic hybridization (array CGH). We performed quantitative-PCR validation on a subset of the calls that differed from the array CGH data sets. Availability: The software described in this manuscript, named cnD for copy number detector, is free and released under the GPL. The program is implemented in the D programming language using the Tango library. Source code and pre-compiled binaries are available at http://www.sanger.ac.uk/resources/software/cnd.html Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

Tumorigenesis is a multi-step process in which normal cells transform into malignant tumors following the accumulation of genetic mutations that enable them to evade the growth control checkpoints that would normally suppress their growth or result in apoptosis. It is therefore important to identify those combinations of mutations that collaborate in cancer development and progression. DNA copy number alterations (CNAs) are one of the ways in which cancer genes are deregulated in tumor cells. We hypothesized that synergistic interactions between cancer genes might be identified by looking for regions of co-occurring gain and/or loss. To this end we developed a scoring framework to separate truly co-occurring aberrations from passenger mutations and dominant single signals present in the data. The resulting regions of high co-occurrence can be investigated for between-region functional interactions. Analysis of high-resolution DNA copy number data from a panel of 95 hematological tumor cell lines correctly identified co-occurring recombinations at the T-cell receptor and immunoglobulin loci in T- and B-cell malignancies, respectively, showing that we can recover truly co-occurring genomic alterations. In addition, our analysis revealed networks of co-occurring genomic losses and gains that are enriched for cancer genes. These networks are also highly enriched for functional relationships between genes. We further examine sub-networks of these networks, core networks, which contain many known cancer genes. The core network for co-occurring DNA losses we find seems to be independent of the canonical cancer genes within the network. Our findings suggest that large-scale, low-intensity copy number alterations may be an important feature of cancer development or maintenance by affecting gene dosage of a large interconnected network of functionally related genes.

SUMMARY: Insertional mutagenesis is a powerful method for gene discovery. To identify the location of insertion sites in the genome linker based polymerase chain reaction (PCR) methods (such as splinkerette-PCR) may be employed. We have developed a web application called iMapper (Insertional Mutagenesis Mapping and Analysis Tool) for the efficient analysis of insertion site sequence reads against vertebrate and invertebrate Ensembl genomes. Taking linker based sequences as input, iMapper scans and trims the sequence to remove the linker and sequences derived from the insertional mutagen. The software then identifies and removes contaminating sequences derived from chimeric genomic fragments, vector or the transposon concatamer and then presents the clipped sequence reads to a sequence mapping server which aligns them to an Ensembl genome. Insertion sites can then be navigated in Ensembl in the context of genomic features such as gene structures. iMapper also generates test-based format for nucleic acid or protein sequences (FASTA) and generic file format (GFF) files of the clipped sequence reads and provides a graphical overview of the mapped insertion sites against a karyotype. iMapper is designed for high-throughput applications and can efficiently process thousands of DNA sequence reads. AVAILABILITY: iMapper is web based and can be accessed at http://www.sanger.ac.uk/cgi-bin/teams/team113/imapper.cgi.