BOL: Related items

Manthey Research Group – Evolutionary Genomics

Thu, 22 Aug 2024 06:25:55 -0500

We focus on fundamental questions in genomics, ecology, and evolution. Our methods include fieldwork and labwork, but most of our time is spent analyzing genomics data using computational biology approaches.

Ant / bacteria co-evolution, landscape genomics, and population genomics
Vertebrate and/or invertebrate genome evolution

If you might be interested in joining our research group, send an email with your intent and why this group would potentially be a good fit for your future goals along with a CV / Resume to jdmanthey (at) gmail (dot) com

More at https://mantheylab.org/

Ten recommendations for creating usable bioinformatics command line software

RAJESH DETROJA — Sun, 08 Jun 2014 10:06:26 -0500

Bioinformatics software varies greatly in quality. In terms of usability, the command line interface is the first experience a user will have of a tool. Unfortunately, this is often also the last time a tool will be used. Here I present ten recommendations for command line software author’s tools to follow, which I believe would greatly improve the uptake and usability of their products, waste less user’s time, and improve the quality of scientific analyses.

Address of the bookmark: http://www.gigasciencejournal.com/content/2/1/15?utm_content=buffer25ee0&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

List of cancer genomics research web resources !

biogeek — Wed, 27 Dec 2017 20:33:09 -0600

Major web resources for cancer genomics research

CGHub
https://cghub.ucsc.edu/
Comprehensive data repository; huge data size

EGA
https://www.ebi.ac.uk/ega/
Comprehensive data repository; huge data size

COSMIC
http://cancer.sanger.ac.uk
Largest somatic mutation database; genome sequencing paper curation

CPRG
http://www.broadinstitute.org/software/cprg
Interface for cancer program resources

GDAC
http://gdac.broadinstitute.org/
Data analysis; automatic pipelines; user-friendly reports

SNP500Cancer
http://snp500cancer.nci.nih.gov
Sequence and genotype verification of SNPs

canEvolve
www.canevolve.org/
Comprehensive analysis of tumor profile; Data from 90 studies involving more than 10,000 patients

MethyCancer
http://methycancer.psych.ac.cn
Relationship among DNA methylation, gene expression and cancer

SomamiR
http://compbio.uthsc.edu/SomamiR/
Correlation between somatic mutation and microRNA; genome-wide displaying

cBioPortal
http://www.cbioportal.org/public-portal/
Graphical summaries; gene alteration; processed data; visualization

UCSC Cancer Genomics Browser
https://genome-cancer.soe.ucsc.edu/
Clinical information; gene expression; copy number variation; visualization

CGWB
https://cgwb.nci.nih.gov/
Visualization; gene mutation and variation; automated analysis pipeline

GDSC
http://www.cancerrxgene.org
Drug sensitivity information; drug response information

canSAR
https://cansar.icr.ac.uk/
Multidisciplinary information; drug discovery

NONCODE
http://www.noncode.org/ ncRNAs;
lncRNAs; up-to-date and comprehensive resource

Bioinformatics algorithms tutorials

John Parker — Tue, 24 Jun 2014 00:10:45 -0500

Useful bioinformatics tutorial, such as

De Bruijn Graphs for NGS Assembly
Algorithms for PacBio Reads
Software and Hardware Concepts for Bioinformatics
Finding us in Homolog.us (Search Algorithms)
NGS Genome and RNAseq Assembly - a Hands on Primer
Introduction to PERL, Python, R and C/C++ for Bioinformatics

Address of the bookmark: http://www.homolog.us/Tutorials/

Where to Aproach for R&D Funds in India ?

Surabhi Chaudhary — Thu, 24 Jun 2021 01:04:30 -0500

Companies and governments do research and development (R&D/ R'n'D/ R+D) to promote innovation in order to produce new goods or services and/or enhance existing product lines. R&D covers all actions inside an organization aimed at boosting innovation, such as creating incubators, assisting innovators in scaling up their ideas, and cultivating an innovation culture.

Here are the list of all the research and development funding agencies in India.

Workshop On Molecular Modeling and Dynamics Simulation Analyses

Fri, 04 Jul 2014 13:38:13 -0500

Workshop On Molecular Modeling and Dynamics Simulation Analyses

August1-2, 2014

Organised By

Centre of Excellence in Bioinformatics
Bioinformatics Infrastructure Facility
Department of Biochemistry
University of Lucknow
Lucknow-226007

Course Contents

Molecular Modeling
Homology Modeling
Molecular Docking
Post-structural Analyses

Molecular Dynamics (MD)
Simulation
Linux Introduction
Gromacs Installation

MD Simulation of Protein ligand complex
Analyses of MD
Trajectories
Visualization of Dynamic
complexes

Important Dates

Registration Begins June 25, 2014
Registration Closes July 25, 2014

Brochure : www.lkouniv.ac.in/conference/Brochure_August,%202014.pdf

lordFAST: sensitive and Fast Alignment Search Tool for LOng noisy Read sequencing Data

BioJoker — Tue, 27 Nov 2018 04:43:57 -0600

lordFAST is a sensitive tool for mapping long reads with high error rates. lordFAST is specially designed for aligning reads from PacBio sequencing technology but provides the user the ability to change alignment parameters depending on the reads and application.

lordFAST, a novel long-read mapper that is specifically designed to align reads generated by PacBio and potentially other SMS technologies to a reference. lordFAST not only has higher sensitivity than the available alternatives, it is also among the fastest and has a very low memory footprint.

Address of the bookmark: https://github.com/vpc-ccg/lordfast

Orione – a web-based framework for NGS analysis in microbiology

Martin Jones — Wed, 23 Jul 2014 06:43:03 -0500

End-to-end NGS microbiology data analysis requires a diversity of tools covering bacterial resequencing, de novo assembly, scaffolding, bacterial RNA-Seq, gene annotation and metagenomics. However, the construction of computational pipelines that use different software packages is difficult due to a lack of interoperability, reproducibility, and transparency. To overcome these limitations researchers at CRS4, Italy have developed Orione, a Galaxy-based framework consisting of publicly available research software and specifically designed pipelines to build complex, reproducible workflows for NGS microbiology data analysis. Enabling microbiology researchers to conduct their own custom analysis and data manipulation without software installation or programming, Orione provides new opportunities for data-intensive computational analyses in microbiology and metagenomics.

Reference

Cuccuru G1, Orsini M, Pinna A, Sbardellati A, Soranzo N, Travaglione A, Uva P, Zanetti G, Fotia G. (2014) Orione, a web-based framework for NGS analysis in microbiology. Bioinformatics [Epub ahead of print]. [article]

Address of the bookmark: http://orione.crs4.it/

Basics of BLAST Programs !

BioStar — Fri, 26 Jul 2024 06:04:26 -0500

The Basic Local Alignment Search Tool (BLAST) is a powerful bioinformatics program used to compare an input sequence (such as DNA, RNA, or protein sequences) against a database of sequences to find regions of similarity. Developed by the National Center for Biotechnology Information (NCBI), BLAST is widely used for identifying species, finding functional and evolutionary relationships between sequences, and predicting the function of novel sequences.

Key Features of BLAST:
1. Sequence Comparison: BLAST searches for local alignments between the query sequence and sequences in a database. It identifies regions of similarity, which can help infer functional and evolutionary relationships.

2. Speed and Efficiency: BLAST uses heuristic algorithms, making it faster than exhaustive search methods, suitable for large-scale database searches.

3. Versatility: There are several versions of BLAST for different types of sequence comparisons:
- blastn: Compares a nucleotide query sequence against a nucleotide sequence database.
- blastp: Compares a protein query sequence against a protein sequence database.
- blastx: Compares a nucleotide query sequence translated in all reading frames against a protein sequence database.
- tblastn: Compares a protein query sequence against a nucleotide sequence database translated in all reading frames.
- tblastx: Compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database.

4. Scoring and E-value: BLAST results are scored based on the quality and length of the alignments. The E-value (expect value) indicates the number of alignments one can expect to find by chance, with lower E-values representing more significant matches.

5. Output Formats: BLAST provides results in various formats, including plain text, HTML, XML, and JSON, making it adaptable for different types of analyses and integrations with other tools.

Applications of BLAST:
- Genomic Research: Identifying genes, understanding genetic diversity, and mapping genome sequences.
- Protein Function Prediction: Inferring the function of unknown proteins by comparing them to known protein sequences.
- Evolutionary Studies: Exploring evolutionary relationships between organisms by comparing their genetic material.
- Medical Research: Identifying pathogens, understanding disease mechanisms, and developing treatments by comparing sequences of interest.

Overall, BLAST is an essential tool in bioinformatics, offering a reliable and efficient way to analyze and interpret biological sequence data.

Breaking chromosomes to study cancer !!!

Jit — Fri, 18 Jul 2014 05:42:09 -0500

Chromosomes are present in every cell of our body and they contain the information the body needs to develop and function properly. This information is carried in genes that are arranged along the chromosomes. There are usually 46 chromosomes in every cell. These chromosomes come in pairs, one from our mother and one from our father. The chromosomes can be sorted into 23 pairs by looking at them down a microscope.

Most people who have a balanced translocation have the right amount of chromosome material but it has been rearranged in some way. This may happen if two chromosomes swap pieces (a reciprocal translocation). In other cases two whole chromosomes may become stuck together (a Robertsonian translocation). This page describes what happens when someone has a reciprocal translocation.

Reciprocal chromosomal translocations occur following double-strand breaks (DSBs) in DNA when a section of one chromosome is exchanged with that of another, non-homologous chromosome. These exchanges may produce a dysfunctional fusion gene that disrupts cell growth and survival pathways, such as the translocations seen in leukemia and childhood sarcomas.

Chromosomal translocations have been well studied in cancer cell lines which are associated with two types of cancer, acute myeloid leukemia and Ewing's sarcoma, but determining how they contribute to cancer development is complicated by additional mutations and altered gene expression profiles in these cultured cells. Now, Juan Carlos Ramirez, head of the Viral Vector Facility at the Fundacion Centro Nacional de Investigaciones Cardiovasculares (CNIC) and his colleagues Raul Torres at CNIC and Sandra Rodriguez-Peralez at the Spanish National Cancer Center (CNIO) in Madrid, Spain have used a new genome editing tool, CRISPR-Cas9, to induce chromosomal translocations for the first time in a human cell line and in primary cells. The study's authors conclude by stating that the use of this technology will allow for the clarification of how and why chromosomal translocation occurs, which without doubt will allow new anti-cancer therapeutic strategies to be tackled.

Using RNA-Guided Endonuclease (RGEN) technology or CRISPR/Cas9 genome engineering technology, CNIO and CNIC researchers have shown that it is possible to obtain such chromosomal translocations. The CRISPR-Cas9 system is extremely simple to introduce a cut at the desired locus, easier to design, and cheaper than many other systems. Using the CRISPR-Cas9 system, Ramirez and his colleagues reproduced the translocations observed in Ewing’s Sarcoma (ES) and Acute Myeloid Leukemia (AML) patient cell lines in HEK293 cells and also generated the ES translocation in human mesenchymal stem cells and the AML translocation in umbilical cord blood cells.

By focusing on chromosomal translocation without the confounding characteristics of established cell lines, these new cells lines should help answer the fundamental question of what causes a cell to become cancerous. Ramirez and his team now look forward to modeling other chromosome translocations in a variety of cell types.

Reference:

http://en.wikipedia.org/wiki/Chromosomal_translocation

http://www.nature.com/ncomms/2014/140603/ncomms4964/abs/ncomms4964.html