Address of the bookmark: https://sourceforge.net/projects/ksnp/files/

• multi-class semantic segmentation using U-Net architecture
• multi-class object detection using YOLOv3 architecture

You can install the latest version of platypus with remotes package:

Note that in order to install platypus you need to install keras and tensorflow packages and Tensorflow version >= 2.0.0 ( Tensorflow 1.x will not be supported!)

Address of the bookmark: https://github.com/maju116/platypus

Wgsim is a small tool for simulating sequence reads from a reference genome. It is able to simulate diploid genomes with SNPs and insertion/deletion (INDEL) polymorphisms, and simulate reads with uniform substitution sequencing errors. It does not generate INDEL sequencing errors, but this can be partly compensated by simulating INDEL polymorphisms.

Wgsim outputs the simulated polymorphisms, and writes the true read coordinates as well as the number of polymorphisms and sequencing errors in read names. One can evaluate the accuracy of a mapper or a SNP caller with wgsim_eval.pl that comes with the package.

Address of the bookmark: https://github.com/lh3/wgsim

Here is how I use the blasr to align PacBio reads to the contigs (target.fasta). The “target.fasta.sa” is the suffix array from “target.fasta” generated by sawriter.

blasr query.fa ./target.fasta -sa ./target.fasta.sa -bestn 40 -maxScore -500 -m 4 -nproc 24 -out target.m4 -maxLCPLength 15

the output format option “-m 4″ generate the alignment coordinate. Not fully documented, but I can explain that to you. 

I use a 24 cores / 48G ram server for the alignment. It took about 2 to 3 hours aligning 3G PacBio Reads to 10^6 sequences of short read contigs with a mean 3.5kbp length.

Address of the bookmark: http://bix.ucsd.edu/projects/blasr/

Mulan (http://mulan.dcode.org/), a novel method and a network server for comparing multiple draft and finished-quality sequences to identify functional elements conserved over evolutionary time. Mulan brings together several novel algorithms: the TBA multi-aligner program for rapid identification of local sequence conservation, and the multiTF program for detecting evolutionarily conserved transcription factor binding sites in multiple alignments. In addition, Mulan supports two-way communication with the GALA database; alignments of multiple species dynamically generated in GALA can be viewed in Mulan, and conserved transcription factor binding sites identified with Mulan/multiTF can be integrated and overlaid with extensive genome annotation data using GALA.

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC540288/

https://github.com/shinout/clipcrop

Address of the bookmark: https://github.com/shinout/clipcrop

conda create --name chromatiblock
conda activate chromatiblock
conda install chromatiblock --channel conda-forge --channel bioconda

Then in future to run chromatiblock you can reactivate this environemtn using conda activate chromatiblock

Direct download:

Alternatively you can download and run the script from here.

Address of the bookmark: https://github.com/mjsull/chromatiblock

• SVEngine is a multi-purpose and self-contained simulator for whole genome scale spike-in of thousands of SV events of various types in both single-sample and matched sample scenarios.
• SVEngine takes as input reference contigs in FASTA files, variant meta distribution as specified in META files (see Manual) or specific variant information as specified in VAR files (see Manual) and NEWICK files for specifying clonal phylogenetic trees in cancer.
• SVEngine outpus alterred contigs in FASTA files, spiked-in variants in VAR files (see Manual), simulated short read in FASTQ files and aligned short reads in BAM files.

Address of the bookmark: https://bitbucket.org/charade/svengine/src/master/

Address of the bookmark: https://github.com/codialab/GFAffix

Run Maq Now

Follow these steps to try Maq. All you need is a reference sequence file in the FASTA format.

1. Prepare a reference sequence (ref.fasta). Better a bacterial genome.
2. Download maq, maq-data and maqview at the download page.
3. Copy maq, maq.pl and maq_eval.pl to the $PATH or to the same directory.
4. Simulate diploid reference and read sequences, map reads, call variants and evaluate the results in one go:

   maq.pl demo ref.fasta calib-30.dat
   

   where calib-30.dat is contained in maq-data.
5. View the alignment:

   cd maqdemo/easyrun;
   maqindex -i -c consensus.cns all.map;
   maqview -c consensus.cns all.map

Even for advanced maq users, running `maq.pl demo' is recommended. You may find something helpful.

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