Address of the bookmark: https://github.com/martinjvickers/KAST

With advances in Cancer Genomics, Mutation Annotation Format (MAF) is being widley accepted and used to store variants detected. The Cancer Genome Atlas Project has seqenced over 30 different cancers with sample size of each cancer type being over 200. The resulting data consisting of genetic variants is stored in the form of Mutation Annotation Format. This package attempts to summarize, analyze, annotate and visualize MAF files in an efficient manner either from TCGA sources or any in-house studies as long as the data is in MAF format. Maftools can also handle ICGC Simple Somatic Mutation format.

maftools is on bioRxiv

Please cite the below if you find this tool useful for you.

Mayakonda, A. and H.P. Koeffler, Maftools: Efficient analysis, visualization and summarization of MAF files from large-scale cohort based cancer studies. bioRxiv, 2016. doi: http://dx.doi.org/10.1101/052662

Address of the bookmark: https://github.com/PoisonAlien/maftools

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.

• optimized to work with vast amount of data and very large file sets (~ 10,000 - 100,000).
• allows the selective retrieval of only the required files (file masks, ls-lR parsing, recursive search, updates)
• has a built-in repository containing the settings for the most common bioinformatics download needs
• built-in wizard for batch post-processing of downloaded files (archive extraction, file conversion, etc.)
• capable of performing multiple download or update tasks simultaneously

BioDownloader has a built-in repository containing the settings for common bioinformatics file-synchronization needs, including the Protein Data Bank (PDB) and National Center for Biotechnology Information (NCBI) databases. It can post-process downloaded files, including archive extraction and file conversions.

http://dunbrack.fccc.edu/BioDownloader/

Address of the bookmark: http://dunbrack.fccc.edu/BioDownloader/

Address of the bookmark: https://github.com/tanghaibao/quota-alignment

http://www.bx.psu.edu/~ratan/

Address of the bookmark: http://www.bx.psu.edu/miller_lab/

To cite Mugsy, use:

Angiuoli SV and Salzberg SL. Mugsy: Fast multiple alignment of closely related whole genomes.Bioinformatics 2011 27(3):334-4

Address of the bookmark: http://mugsy.sourceforge.net/

Using the reference sequence dictionary (*.dict), it also creates some empty BAM files if no sam record was found for a chromosome. A pair of 'mock' SAM-Records can also be added to those empty BAMs to avoid some tools (like samtools) to crash.

Usage

java -jar splitbam.jar -p OUT/__CHROM__/__CHROM__.bam -R ref.fasta (bam|sam|stdin)

Options

• -h help; This screen.
• -R (indexed reference file) REQUIRED.
• -u (unmapped chromosome name): default:Unmapped
• -e | --empty : generate EMPTY bams for chromosome having no read mapped
• -m | --mock : if option '-e', add a mock pair of sam records to the empty bam
• -p (output file/bam pattern) REQUIRED. MUST contain __CHROM__ and end with .bam
• -s assume input is sorted.
• -x | --index create index.
• -t | --tmp (dir) tmp file directory
• -G (file) chrom-group file (see below)

Address of the bookmark: https://code.google.com/archive/p/jvarkit/wikis/SplitBam.wiki

more at https://ccb.jhu.edu/software/hisat2/index.shtml

Address of the bookmark: https://github.com/infphilo/hisat2

To use CLgenomics, individual genome data (genome sequences + annotation details) are compiled and saved in a specially formatted file called CLG (ChunLab Genomics). Each CLG file corresponds with one bacterial genome. If multiple genomes are being considered and compared, multiple CLG files are needed. ChunLab offers >40,000 CLG files of publicly available Bacterial and Archaeal genomes.

Address of the bookmark: https://chunlab.wordpress.com/clgenomics-software/