BOL: Related items

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.

Coronavirus Resources !

Neel — Wed, 25 Mar 2020 17:11:33 -0500

2019nCoVR features comprehensive integration of genomic and proteomic sequences as well as their metadata information from the GISAID, NCBI, NMDC and CNCB/NGDC. It also incorporates a wide range of relevant information including scientific literatures, news, and popular articles for science dissemination, and provides visualization functionalities for genome variation analysis results based on all collected 2019-nCoV strains.

Annotation

https://bigd.big.ac.cn/ncov/variation/annotation

Genome wharehouse

https://bigd.big.ac.cn/gwh/browse/index

Released Genome

https://bigd.big.ac.cn/ncov/release_genome

Download data

ftp://download.big.ac.cn/Genome/Viruses/Coronaviridae/

Raw data

https://bigd.big.ac.cn/gsa/browse/run/?tag=Coronaviridae

Address of the bookmark: https://bigd.big.ac.cn/ncov/about

RITA: Rapid identification of high-confidence taxonomic assignments for metagenomic data

Jit — Mon, 27 Nov 2017 08:25:33 -0600

RITA is a standalone software package and Web server for taxonomic assignment of metagenomic sequence reads. By combining homology predictions from BLAST or UBLAST with compositional classifications from a Naive Bayes classifier, RITA is able to achieve very high accuracy on short reads. Unlike other hybrid approaches which combine these predictions for all sequences to be classified, RITA uses a pipeline to first identify cases where both types of classifier are in agreement, which constitute the highest-confidence set. Sequences not classified in this manner are subjected to a series of downstream classification steps.

This work has been accepted for publication:

MacDonald NJ, Parks DH, and Beiko RG. Rapid identification of taxonomic assignments. Accepted to Nucleic Acids Research April 4, 2012.

If you have any questions or bug reports, please let us know at .

Address of the bookmark: http://kiwi.cs.dal.ca/Software/RITA

fastv - detect virus

Jit — Sat, 11 Dec 2021 08:04:10 -0600

fastv is an ultra-fast tool for identification of SARS-CoV-2 and other microbes from sequencing data. It detects microbial sequences from FASTQ data, generates JSON reports and visualizes the result in HTML reports. This tool can be used to detect viral infectious diseases, like COVID-19. This tool supports both short reads (Illumina, BGI, etc.) and long reads (ONT, PacBio, etc.)

Address of the bookmark: https://github.com/OpenGene/fastv

SISRS: Site Identification from Short Read Sequences

Abhimanyu Singh — Wed, 28 Nov 2018 08:56:03 -0600

Next-gen sequence data such as Illumina HiSeq reads. Data must be sorted into folders by taxon (e.g. species or genus). Paired reads in fastq format must be specified by _R1 and _R2 in the (otherwise identical) filenames. Paired and unpaired reads must have a fastq file extension.

Address of the bookmark: https://github.com/rachelss/SISRS

Bio7: an integrated development environment for ecological modeling, scientific image analysis and statistical analysis

Nidhi Rajput — Fri, 07 Feb 2020 23:32:24 -0600

The application Bio7 is an integrated development environment for ecological modeling, scientific image analysis and statistical analysis. The application itself is based on an RCP-Eclipse-Environment (Rich-Client-Platform) which offers a huge flexibility in configuration and extensibility because of its plug-in structure and the possibility of customization.

https://bio7.org/about/

Address of the bookmark: https://bio7.org/home-2/

SOWDHAMINI Lab

Sun, 15 Sep 2013 09:19:12 -0500

Genome sequencing projects have enormous potential for benefiting human endeavors. However, just as acquiring a language's vocabulary does not enable one to speak it, databases that list the amino acid composition of proteins do not directly tell us much about these proteins' higher-level structure and function. The most productive way to indirectly exploit these databases has been to start with the small number of proteins that are fully-characterised and to assume that other "similar" proteins will have a related structure and function. Proteins with very similar amino acid sequence are "no-brainers", but the real test, which our group largely focuses on, is to detect the "essential" similarity in proteins whose non-critical sections have experienced random rearrangements during evolution. In such cases functionally similar proteins may have less than 25% sequence overlap.

More @ http://www.ncbs.res.in/sowdhamini/groups_sowdhamini.htm

Check Linux server configuration !!

Rahul Nayak — Tue, 06 May 2014 01:10:57 -0500

Bioinformatician uses servers for computational analysis. Sometime we need to check the server details before running our programs or tools. Here I am showing some basic commands using them you can gather the system/server information.

To check what version of Operating System is installed on the server you can use the following commands:-
=================================================================
1.cat /etc/issue
[root@localhost ~]# cat /etc/issue
Red Hat Enterprise Linux Server release 5.5 (Tikanga)
Kernel \r on an \m

2.cat /etc/redhat-release
[root@localhost ~]# cat /etc/redhat-release
Red Hat Enterprise Linux Server release 5.5 (Tikanga)

3.lsb_release -a
[root@localhost ~]# lsb_release -a
LSB Version:    :core-3.1-ia32:core-3.1-noarch:graphics-3.1-ia32:graphics-3.1-noarch
Distributor ID: RedHatEnterpriseServer
Description:    Red Hat Enterprise Linux Server release 5.5 (Tikanga)
Release:        5.5
Codename:       Tikanga

To check whether the operating system is 32 or 64bit:-
================================
# uname -i
[root@localhost ~]# uname -i
i386
(i386 represents that server is having 32bit operating system)

[root@localhost ~]# uname -i
x86_64
(x86_64 represents that server is having 64bit operating system)

To see the processor/CPU information:-
=============================
# cat /proc/cpuinfo
[root@localhost ~] cat /proc/cpuinfo
processor       : 0
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) CPU            5130 @ 2.00GHz
stepping        : 6
cpu MHz         : 1995.087
cache size      : 4096 KB
physical id     : 0
siblings        : 2
core id         : 0
cpu cores       : 2
apicid          : 0
fdiv_bug        : no
hlt_bug         : no
f00f_bug        : no
coma_bug        : no
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe nx lm constant_tsc pni monitor ds_cpl vmx tm2 ssse3 cx16 xtpr lahf_lm
bogomips        : 3990.17
(Here processor number 0 indicates that the system is having one process(processor number starts with zero))

To check memory information:-
===========================
# free -m
[root@localhost ~]# free -m
             total       used       free     shared    buffers     cached
Mem:          5066       3513       1552          0        612       2319
-/+ buffers/cache:        582       4484
Swap:         1983          0       1983

# cat /proc/meminfo
[root@localhost ~]# cat /proc/meminfo
MemTotal:      5187752 kB
MemFree:       1639300 kB
Buffers:        627024 kB
Cached:        2374944 kB
SwapCached:          0 kB
Active:        2458788 kB
Inactive:       920964 kB
HighTotal:     4325164 kB
HighFree:      1561936 kB
LowTotal:       862588 kB
LowFree:         77364 kB
SwapTotal:     2031608 kB
SwapFree:      2031608 kB
Dirty:             704 kB
Writeback:           0 kB
AnonPages:      377892 kB
Mapped:          35328 kB
Slab:           153036 kB
PageTables:       6316 kB
NFS_Unstable:        0 kB
Bounce:              0 kB
CommitLimit:   4625484 kB
Committed_AS:   977132 kB
VmallocTotal:   116728 kB
VmallocUsed:      4492 kB
VmallocChunk:   112124 kB
HugePages_Total:     0
HugePages_Free:      0
HugePages_Rsvd:      0
Hugepagesize:     2048 kB

To check the model and serial name of the server:-
=======================================
[root@localhost ~]# dmidecode | egrep -i "product name|Serial number"
Product Name: PowerEdge R710
Serial Number: AB8CDE1


To check the host name:-
=====================
[root@localhost ~]# uname -n
localhost

[root@localhost ~]# hostname
localhost

To check the kernel version:-
========================
[root@localhost ~]# uname -r
2.6.18-238.9.1.el5PAE

Bioinformatician's summer vacation !!!

Jit — Wed, 03 Sep 2014 13:11:50 -0500

Yes, the bioinformatician do spend their summer vacation like this. They spend more time on cheking the JOBS running on various servers.

SLURM

Jit — Wed, 04 May 2016 05:13:21 -0500

SLURM workload manager software, a free open-source workload manager designed specifically to satisfy the demanding needs of high performance computing.

This page is a HOWTO guide for setting up a SLURM installation, currently focused on a CentOS 7 Linux OS. Please send feedback to Ole.H.Nielsen /at/ fysik.dtu.dk.

See the SLURM homepage (also https://computing.llnl.gov/linux/slurm/).

Address of the bookmark: https://wiki.fysik.dtu.dk/niflheim/SLURM