Address of the bookmark: https://github.com/schneebergerlab/syri

For knowledge about Linux and their importance amongst bioinformatician plesae read this article "An introduction to Linux for bioinformatics" by Paul Stothard.

Linux cheat sheet at http://bioinformaticsonline.com/file/view/87/linux-cheat-sheet

Please browse for futher useful linux pages on right hand side ...

https://github.com/pditommaso/awesome-pipeline

Address of the bookmark: https://github.com/pditommaso/awesome-pipeline

LPI scores are inversely proportional to the phylogenetic distance between database match sequences and the query genome. These scores are useful not only for large-scalede novo predictions of horizontally transferred proteins, but can also serve as an independent quality control test for potential horizontal transfer candidates identified by alternative methods, especially those based on nucleic acid signatures. Candidates having high LPI scores are unlikely to have been horizontally transferred, since they are highly conserved among closely related organisms.

One unique and powerful feature of the DarkHorse HGT Candidate database is the opportunity to explore the phylogenetic background of potential HGT donors as well as recipients. The breadth of the database allows not only query sequences, but also their database match partners to be evaluated for sequence similarity or novelty compared to taxonomically related organisms.

DarkHorse is configurable for varying degrees of phylogenetic granularity and protein sequence conservation. Users should consult the references cited below for a complete explanation of parameter selection and result interpretation. A brief tutorial page is also available on-line.

Address of the bookmark: http://darkhorse.ucsd.edu/download.html

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

Address of the bookmark: http://christophergandrud.github.io/d3Network/

##################################################
[Mon May 7 11:29:18 2018] Reduction...
#file name genome size contigs heterozygous size [%] heterozygous contigs [%] identity [%] possible joins homozygous size [%] homozygous contigs [%]
/usr/lib/python2.7/dist-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
test/run1/contigs.fa 163897 245 66377 40.50 221 90.20 94.854 0 97520 59.50 24 9.80

##################################################
[Mon May 7 11:29:29 2018] Estimating parameters of libraries...
Aligning 19504 mates per library...
Insert size statistics Mates orientation stats
FastQ files read length median mean stdev FF FR RF RR
test/5000_1.fq.gz test/5000_2.fq.gz 50 4998 4990.20 721.47 0 4674 0 0
test/600_1.fq.gz test/600_2.fq.gz 100 599 598.63 47.68 0 10000 0 0

##################################################
[Mon May 7 11:29:29 2018] Scaffolding...
iteration 1.1: test/run1/contigs.reduced.fa 24 97520 39.355 17 94157 7321 2195 0 29603
19505 pairs. 17302 passed filtering [88.71%]. 1627 in different contigs [8.34%].
1526 pairs. 558 in different contigs [36.57%].
iteration 1.2: test/run1/_sspace.1.1.fa 3 97626 39.344 3 97626 87536 6063 821 87536
19505 pairs. 17607 passed filtering [90.27%]. 182 in different contigs [0.93%].
1077 pairs. 124 in different contigs [11.51%].
iteration 2.1: test/run1/_sspace.1.2.fa 3 97626 39.344 3 97626 87536 6063 821 87536
19505 pairs. 15112 passed filtering [77.48%]. 1295 in different contigs [6.64%].
3417 pairs. 396 in different contigs [11.59%].
iteration 2.2: test/run1/_sspace.2.1.fa 1 99133 39.344 1 99133 99133 99133 2328 99133
19505 pairs. 15152 passed filtering [77.68%]. 0 in different contigs [0.00%].
3398 pairs. 0 in different contigs [0.00%].

##################################################
[Mon May 7 11:29:34 2018] Gap closing...
iteration 1.1: test/run1/scaffolds.fa 1 99133 39.344 1 99133 99133 99133 2328 99133

##################################################
[Mon May 7 11:29:35 2018] Final reduction...
#file name genome size contigs heterozygous size [%] heterozygous contigs [%] identity [%] possible joins homozygous size [%] homozygous contigs [%]
[WARNING] Nothing reduced!
test/run1/scaffolds.filled.fa 99390 1 0 0.00 0 0.00 0.000 0 99390 100.00 1 100.00

##################################################
[Mon May 7 11:29:35 2018] Reporting statistics...
#fname contigs bases GC [%] contigs >1kb bases in contigs >1kb N50 N90 Ns longest
test/contigs.fa 245 163897 40.298 24 117391 3975 233 0 29603
test/run1/contigs.fa 245 163897 40.298 24 117391 3975 233 0 29603
test/run1/contigs.reduced.fa 24 97520 39.355 17 94157 7321 2195 0 29603
test/run1/_sspace.1.1.fa 3 97626 39.344 3 97626 87536 6063 821 87536
test/run1/_sspace.1.2.fa 3 97626 39.344 3 97626 87536 6063 821 87536
test/run1/_sspace.2.1.fa 1 99133 39.344 1 99133 99133 99133 2328 99133
test/run1/_sspace.2.2.fa 1 99133 39.344 1 99133 99133 99133 2328 99133
test/run1/scaffolds.fa 1 99133 39.344 1 99133 99133 99133 2328 99133
test/run1/_gapcloser.1.1.fa 1 99390 39.689 1 99390 99390 99390 2 99390
test/run1/scaffolds.filled.fa 1 99390 39.689 1 99390 99390 99390 2 99390
test/run1/scaffolds.reduced.fa 1 99390 39.689 1 99390 99390 99390 2 99390

##################################################
[Mon May 7 11:29:35 2018] Cleaning-up...
#Time elapsed: 0:00:17.376924

The mmgenome R package also facilitates effortless integration with additional data sources and hence should not be seen as "yet another binning method", but rather a package to integrate different binning strategies.

All functions in the mmgenome R package has associated documentation, check it out in R by e.g. ?mmplot.

Address of the bookmark: https://github.com/MadsAlbertsen/mmgenome

Compare structural and functional annotations of proteins between sequenced genomes.

1. MISA: MISA (MIcroSAtellite) is a web-based tool that can identify SSRs in DNA sequences. It can be used to analyze nucleotide sequences from various organisms and can identify perfect, compound, and imperfect SSRs.

2. SSR Locator: SSR Locator is a web-based tool that identifies SSRs in both DNA and RNA sequences. It can identify perfect, compound, and imperfect SSRs, and can also filter out low complexity regions.

3. SciRoKo: SciRoKo is a software tool that can identify SSRs in DNA sequences. It can be used to analyze genomic and transcriptomic sequences from various organisms and can identify perfect, compound, and imperfect SSRs.

4. Primer3: Primer3 is a web-based tool that designs PCR primers for SSRs. It can design primers for perfect and imperfect SSRs, and can be used to design primers for SSRs in various organisms.

5. QDD: QDD (Quick Detection of Duplication) is a software tool that can identify SSRs in DNA sequences and can also identify duplicate loci. It can be used to analyze genomic and transcriptomic sequences from various organisms.

These are just a few examples of the many bioinformatics tools available for exploring SSRs. Depending on your specific needs and research questions, you may find that other tools are more appropriate for your analysis.