Complete Genome Sequence of a Virulent Leptospira interrogans Serovar Copenhageni Strain, Assembled with a Combination of Nanopore and Illumina Reads

Here, we present the complete genome sequence of a highly virulent Leptospira interrogans serovar Copenhageni strain isolated from a dog with severe leptospirosis. In this work, a gapless genome draft was assembled with a combination of Nanopore and Illumina data of relatively low coverage.Here, we present the complete genome sequence of a highly virulent Leptospira interrogans serovar Copenhageni strain isolated from a dog with severe leptospirosis. In this work, a gapless genome draft was assembled with a combination of Nanopore and Illumina data of relatively low coverage

Augur: a bioinformatics toolkit for phylogenetic analyses of human pathogens

The analysis of human pathogens requires a diverse collection of bioinformatics tools. These tools include standard genomic and phylogenetic software and custom software developed to handle the relatively numerous and short genomes of viruses and bacteria. Researchers increasingly depend on the outputs of these tools to infer transmission dynamics of human diseases and make actionable recommendations to public health officials (Black et al., 2020; Gardy et al., 2015). In order to enable real-time analyses of pathogen evolution, bioinformatics tools must scale rapidly with the number of samples and be flexible enough to adapt to a variety of questions and organisms. To meet these needs, we developed Augur, a bioinformatics toolkit designed for phylogenetic analyses of human pathogens

Forensic SNP genotyping using nanopore MinION sequencing

One of the latest developments in next generation sequencing is the Oxford Nanopore Technologies' (ONT) MinION nanopore sequencer. We studied the applicability of this system to perform forensic genotyping of the forensic female DNA standard 9947 A using the 52 SNP-plex assay developed by the SNPforID consortium. All but one of the loci were correctly genotyped. Several SNP loci were identified as problematic for correct and robust genotyping using nanopore sequencing. All these loci contained homopolymers in the sequence flanking the forensic SNP and most of them were already reported as problematic in studies using other sequencing technologies. When these problematic loci are avoided, correct forensic genotyping using nanopore sequencing is technically feasible

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry identification of \u3ci\u3eMoraxella bovoculi\u3c/i\u3e and \u3ci\u3eMoraxella bovis\u3c/i\u3e isolates from cattle

Infectious bovine keratoconjunctivitis (IBK) is an economically significant disease caused by Moraxella bovis. Moraxella bovoculi, although not reported to cause IBK, has been isolated from the eyes of cattle diagnosed with IBK. Identification of M. bovis and M. bovoculi can be performed using biochemical or DNA-based approaches, both of which may be time consuming and inconsistent between laboratories. We conducted a comparative evaluation of M. bovoculi and M. bovis identification using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with a database provided by Bruker Daltonics (termed the BDAL database), the BDAL database supplemented with spectra generated in our study (termed the UNLVDC database), and with PCR–restriction-fragment length polymorphism (PCR-RFLP) typing. M. bovoculi (n = 250) and M. bovis (n = 18) isolates from cattle with or without IBK were used. MALDI-TOF MS using the UNLVDC database correctly identified 250 of 250 (100%) of M. bovoculi and 17 of 18 (94%) of M. bovis isolates. With the BDAL database, MALDI-TOF MS correctly identified 249 of 250 (99%) of M. bovoculi and 7 of 18 (39%) of M. bovis isolates. In comparison, the PCR-RFLP test correctly identified 210 of 250 (84%) of M. bovoculi and 12 of 18 (66%) of M. bovis isolates. Thus, MALDI-TOF MS with the UNLVDC database was the most effective identification methodology for M. bovis and M. bovoculi isolates from cattle

A method for identification of the methylation level of CpG islands from NGS data

In the course of sample preparation for Next Generation Sequencing (NGS), DNA is fragmented by various methods. Fragmentation shows a persistent bias with regard to the cleavage rates of various dinucleotides. With the exception of CpG dinucleotides the previously described biases were consistent with results of the DNA cleavage in solution. Here we computed cleavage rates of all dinucleotides including the methylated CpG and unmethylated CpG dinucleotides using data of the Whole Genome Sequencing datasets of the 1000 Genomes project. We found that the cleavage rate of CpG is significantly higher for the methylated CpG dinucleotides. Using this information, we developed a classifier for distinguishing cancer and healthy tissues based on their CpG islands statuses of the fragmentation. A simple Support Vector Machine classifier based on this algorithm shows an accuracy of 84%. The proposed method allows the detection of epigenetic markers purely based on mechanochemical DNA fragmentation, which can be detected by a simple analysis of the NGS sequencing data

Helmsman: fast and efficient mutation signature analysis for massive sequencing datasets

Abstract Background The spectrum of somatic single-nucleotide variants in cancer genomes often reflects the signatures of multiple distinct mutational processes, which can provide clinically actionable insights into cancer etiology. Existing software tools for identifying and evaluating these mutational signatures do not scale to analyze large datasets containing thousands of individuals or millions of variants. Results We introduce Helmsman, a program designed to perform mutation signature analysis on arbitrarily large sequencing datasets. Helmsman is up to 300 times faster than existing software. Helmsman’s memory usage is independent of the number of variants, resulting in a small enough memory footprint to analyze datasets that would otherwise exceed the memory limitations of other programs. Conclusions Helmsman is a computationally efficient tool that enables users to evaluate mutational signatures in massive sequencing datasets that are otherwise intractable with existing software. Helmsman is freely available at https://github.com/carjed/helmsman .https://deepblue.lib.umich.edu/bitstream/2027.42/146537/1/12864_2018_Article_5264.pd

Fibronectin and androgen receptor expression data in prostate cancer obtained from a RNA-sequencing bioinformatics analysis

Prostate cancer is the second most commonly diagnosed male cancer in the world. The molecular mechanisms underlying its development and progression are still unclear. Here we show analysis of a prostate cancer RNA-sequencing dataset that was originally generated by Ren et al. [3] from the prostate tumor and adjacent normal tissues of 14 patients. The data presented here was analyzed using our RNA-sequencing bioinformatics analysis pipeline implemented on the bioinformatics web platform, Galaxy. The relative expression of fibronectin (FN1) and the androgen receptor (AR) were calculated in fragments per kilobase of transcript per million mapped reads, and represented in FPKM unit. A subanalysis is also shown for data from three patients, that includes the relative expression of FN1 and AR and their fold change. For interpretation and discussion, please refer to the article, “miR-1207-3p regulates the androgen receptor in prostate cancer via FNDC1/fibronectin” [1] by Das et al

COVID-profiler: a webserver for the analysis of SARS-CoV-2 sequencing data.

BACKGROUND: SARS-CoV-2 virus sequencing has been applied to track the COVID-19 pandemic spread and assist the development of PCR-based diagnostics, serological assays, and vaccines. With sequencing becoming routine globally, bioinformatic tools are needed to assist in the robust processing of resulting genomic data. RESULTS: We developed a web-based bioinformatic pipeline ("COVID-Profiler") that inputs raw or assembled sequencing data, displays raw alignments for quality control, annotates mutations found and performs phylogenetic analysis. The pipeline software can be applied to other (re-) emerging pathogens. CONCLUSIONS: The webserver is available at http://genomics.lshtm.ac.uk/ . The source code is available at https://github.com/jodyphelan/covid-profiler