Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

MicroGMT: A Mutation Tracker for SARS-CoV-2 and Other Microbial Genome Sequences.

ABSTRACT: With the continued spread of SARS-CoV-2 virus around the world, researchers often need to quickly identify novel mutations in newly sequenced SARS-CoV-2 genomes for studying the molecular evolution and epidemiology of the virus. We have developed a Python package, MicroGMT, which takes either raw sequence reads or assembled genome sequences as input and compares against database sequences to identify and characterize indels and point mutations. Although our default setting is optimized for SARS-CoV-2 virus, the package can be also applied to any other microbial genomes. The software is freely available at Github URL https://github.com/qunfengdong/MicroGMT.

SUBMITTER: Xing Y

PROVIDER: S-EPMC7330013 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

MicroGMT: A Mutation Tracker for SARS-CoV-2 and Other Microbial Genome Sequences.

Xing Yue Y Li Xiao X Gao Xiang X Dong Qunfeng Q

Frontiers in microbiology 20200625

With the continued spread of SARS-CoV-2 virus around the world, researchers often need to quickly identify novel mutations in newly sequenced SARS-CoV-2 genomes for studying the molecular evolution and epidemiology of the virus. We have developed a Python package, MicroGMT, which takes either raw sequence reads or assembled genome sequences as input and compares against database sequences to identify and characterize indels and point mutations. Although our default setting is optimized for SARS- ...[more]

PMID: 32670259

Similar Datasets

CovMT: an interactive SARS-CoV-2 mutation tracker, with a focus on critical variants.

Project description: Not available

| S-EPMC7906688 | biostudies-literature

Mutation Profile of SARS-CoV-2 Genome Sequences Originating from Eight Israeli Patient Isolates.

Project description:We report the genome sequences and the identification of genetic variations in eight clinical samples of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Samples were collected from nasopharyngeal swabs of symptomatic and asymptomatic individuals from five care homes for elderly and infirm persons in Israel. The sequences obtained are valuable, as they carry a newly reported nonsynonymous substitution located within the nucleoprotein open reading frame.

| S-EPMC8407753 | biostudies-literature

Near-Complete Genome Sequences of Nine SARS-CoV-2 Strains Harboring the D614G Mutation in Malaysia.

Project description:Here, we report the nearly complete genome sequences of nine severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with the D614G mutation. These viruses were detected from various infected individuals with different levels of severity from Pahang, Malaysia. In addition, this study described the presence of lineage B.1.351 as a type of variant of concern (VOC) and lineages B.1.466.2 and B.1.524 as local variants.

| S-EPMC8340856 | biostudies-literature

Classification of SARS-CoV-2 viral genome sequences using Neurochaos Learning.

Project description:The high spread rate of SARS-CoV-2 virus has put the researchers all over the world in a demanding situation. The need of the hour is to develop novel learning algorithms that can effectively learn a general pattern by training with fewer genome sequences of coronavirus. Learning from very few training samples is necessary and important during the beginning of a disease outbreak when sequencing data is limited. This is because a successful detection and isolation of patients can curb the spread of the virus. However, this poses a huge challenge for machine learning and deep learning algorithms as they require huge amounts of training data to learn the pattern and distinguish from other closely related viruses. In this paper, we propose a new paradigm - Neurochaos Learning (NL) for classification of coronavirus genome sequence that addresses this specific problem. NL is inspired from the empirical evidence of chaos and non-linearity at the level of neurons in biological neural networks. The average sensitivity, specificity and accuracy for NL are 0.998, 0.999 and 0.998 respectively for the multiclass classification problem (SARS-CoV-2, Coronaviridae, Metapneumovirus, Rhinovirus and Influenza) using leave one out crossvalidation. With just one training sample per class for 1000 independent random trials of training, we report an average macro F1-score [Formula: see text] for the classification of SARS-CoV-2 from SARS-CoV-1 genome sequences. We compare the performance of NL with K-nearest neighbours (KNN), logistic regression, random forest, SVM, and naïve Bayes classifiers. We foresee promising future applications in genome classification using NL with novel combinations of chaotic feature engineering and other machine learning algorithms.

| S-EPMC9170350 | biostudies-literature

Complete Genome Sequences of SARS-CoV-2 Strains Detected in Malaysia.

Project description:We sequenced four severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from Malaysia during the second wave of infection and found unique mutations which suggest local evolution. Circulating Malaysian strains represent introductions from different countries, particularly during the first wave of infection. Genome sequencing is important for understanding local epidemiology.

| S-EPMC7225546 | biostudies-literature

Whole-Genome Sequences of SARS-CoV-2 Isolates from Ethiopian Patients.

Project description:Three complete severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from Ethiopian patients were compared with deposited global genomes. Two genomes belonged to genetic group 20A/B.1/GH, and the other belonged to genetic group 20A/B.1.480/GH. Enhancing genomic capacity is important to investigate the transmission and to monitor the evolution and mutational patterns of SARS-CoV-2 in this country.

| S-EPMC8459669 | biostudies-literature

Genome Sequences of SARS-CoV-2 Strains Detected in Hong Kong.

Project description:We sequenced severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from deep throat saliva samples of three imported cases in Hong Kong by Nanopore sequencing. Epidemiological and clinical features of these coronavirus disease 2019 (COVID-19) cases were presented for genomic epidemiology studies.

| S-EPMC7393966 | biostudies-literature

Four SARS-CoV-2 Genome Sequences from Late April in Stockholm, Sweden, Reveal a Rare Mutation in the Spike Protein.

Project description:Here, we report four coding-complete severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome sequences from Stockholm, Sweden, sampled in late April 2020. A rare variant at bp 23463 of the SARS-CoV-2 genome was found, which corresponds to the S1 subunit of the spike protein, changing an arginine (R) residue to histidine (H).

| S-EPMC7453295 | biostudies-literature

Coding-Complete Genome Sequences and Mutation Profiles of Nine SARS-CoV-2 Strains Detected from COVID-19 Patients in Bangladesh.

Project description:Here, we report the coding-complete genome sequences of nine clinical severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants and their mutations. The samples were collected from nine Bangladeshi coronavirus disease 2019 (COVID-19) patients. We have identified the E484K escape mutation and the S359T mutation within the spike protein coding region of the sequenced genomes.

| S-EPMC7953292 | biostudies-literature

Two SARS-CoV-2 Genome Sequences of Isolates from Rural U.S. Patients Harboring the D614G Mutation, Obtained Using Nanopore Sequencing.

Project description:Two coding-complete sequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were obtained from samples from two patients in Arkansas, in the southeastern corner of the United States. The viral genome was obtained using the ARTIC Network protocol and Oxford Nanopore Technologies sequencing.

| S-EPMC8407695 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data