Address of the bookmark: https://github.com/combogenomics/medusa

➜ bloomfilter git:(master) ✗ swig -Wall -c++ -perl5 BloomFilter.i
➜ bloomfilter git:(master) ✗ g++ -c BloomFilter_wrap.cxx -I/home/urbe/anaconda3/lib/perl5/5.22.0/x86_64-linux-thread-multi/CORE/ -fPIC -Dbool=char -O3
BloomFilter_wrap.cxx:1892:30: fatal error: ../BloomFilter.hpp: No such file or directory
compilation terminated.
➜ bloomfilter git:(master) ✗ cd swig
➜ swig git:(master) ✗ g++ -c BloomFilter_wrap.cxx -I/home/urbe/anaconda3/lib/perl5/5.22.0/x86_64-linux-thread-multi/CORE/ -fPIC -Dbool=char -O3
In file included from BloomFilter_wrap.cxx:1877:0:
../BloomFilter.hpp: In member function ‘void BloomFilter::loadHeader(FILE*)’:
../BloomFilter.hpp:141:59: warning: ignoring return value of ‘size_t fread(void*, size_t, size_t, FILE*)’, declared with attribute warn_unused_result [-Wunused-result]
fread(&header, sizeof(struct FileHeader), 1, file);
^
➜ swig git:(master) ✗ g++ -Wall -shared BloomFilter_wrap.o -o BloomFilter.so -O3
➜ swig git:(master) ✗ cd ..
➜ bloomfilter git:(master) ✗ cd ..
➜ lib git:(master) ✗ cd ..
➜ bin git:(master) ✗ ./LINKS
Usage: ./LINKS [v1.8.6]
-f sequences to scaffold (Multi-FASTA format, required)
-s file-of-filenames, full path to long sequence reads or MPET pairs [see below] (Multi-FASTA/fastq format, required)
-m MPET reads (default -m 1 = yes, default = no, optional)
! DO NOT SET IF NOT USING MPET. WHEN SET, LINKS WILL EXPECT A SPECIAL FORMAT UNDER -s
! Paired MPET reads in their original outward orientation <- -> must be separated by ":"
>template_name
ACGACACTATGCATAAGCAGACGAGCAGCGACGCAGCACG:ATATATAGCGCACGACGCAGCACAGCAGCAGACGAC
-d distance between k-mer pairs (ie. target distances to re-scaffold on. default -d 4000, optional)
Multiple distances are separated by comma. eg. -d 500,1000,2000,3000
-k k-mer value (default -k 15, optional)
-t step of sliding window when extracting k-mer pairs from long reads (default -t 2, optional)
Multiple steps are separated by comma. eg. -t 10,5
-o offset position for extracting k-mer pairs (default -o 0, optional)
-e error (%) allowed on -d distance e.g. -e 0.1 == distance +/- 10% (default -e 0.1, optional)
-l minimum number of links (k-mer pairs) to compute scaffold (default -l 5, optional)
-a maximum link ratio between two best contig pairs (default -a 0.3, optional)
*higher values lead to least accurate scaffolding*
-z minimum contig length to consider for scaffolding (default -z 500, optional)
-b base name for your output files (optional)
-r Bloom filter input file for sequences supplied in -s (optional, if none provided will output to .bloom)
NOTE: BLOOM FILTER MUST BE DERIVED FROM THE SAME FILE SUPPLIED IN -f WITH SAME -k VALUE
IF YOU DO NOT SUPPLY A BLOOM FILTER, ONE WILL BE CREATED (.bloom)
-p Bloom filter false positive rate (default -p 0.001, optional; increase to prevent memory allocation errors)
-x Turn off Bloom filter functionality (-x 1 = yes, default = no, optional)
-v Runs in verbose mode (-v 1 = yes, default = no, optional)

Error: Missing mandatory options -f and -s.

ERROR fixed

perl: symbol lookup error: /home/urbe/Tools/LINKS_new/bin/./lib/bloomfilter/swig/BloomFilter.so: undefined symbol: Perl_Gthr_key_ptr

You can also add your favourite NGS educational material, or workshop tutorial by commenting on this bookmarks for user benefit. 

Understanding the basics of genome sequencing:

Tutorial by Luke Jostins.

http://www.genetic-inference.co.uk/blog/2009/04/basics-sequencing-dna-part-1/

http://www.genetic-inference.co.uk/blog/2009/08/basics-sequencing-dna-part-2/

A window into third-generation sequencing

http://hmg.oxfordjournals.org/content/19/R2/R227.full.pdf

==============================================

NGS data analysis pipelines

• Detecting and annotating genetic variations using the HugeSeq pipeline  DOI: 10.1038/nbt.2134
• NARWHAL, a primary analysis pipeline for NGS data http://bioinformatics.oxfordjournals.org/cgi/content/abstract/28/2/284?etoc
• RseqFlow: Workflows for RNA-Seq data analysis  DOI: 10.1093/bioinformatics/btr441
• ngs_backbone: a pipeline for read cleaning, mapping and SNP calling using Next Generation Sequence  10.1186/1471-2164-12-285
• A framework for variation discovery and genotyping using next-generation DNA sequencing data  PubMed: 21478889
• SNiPlay: a web-based tool for detection, management and analysis of SNPs. Application to grapevine diversity projects  DOI: 10.1186/1471-2105-12-134 Abstract: http://www.biomedcentral.com/1471-2105/12/134/abstract
• WEP: a high-performance analysis pipeline for whole-exome data http://www.biomedcentral.com/1471-2105/14/S7/S11
• DDBJ read annotation pipeline: a cloud computing-based pipeline for high-throughput analysis of next-generation sequencing data. http://www.ncbi.nlm.nih.gov/pubmed/23657089
• GATK: a Toolkit for Genome Analysis http://www.broadinstitute.org/gatk/
• Metagenomics:http://www.nbic.nl/education/nbic-phd-school/course-schedule/ngsmetagenomics/
• RNASeq:http://www.nbic.nl/education/nbic-phd-school/course-schedule/ngsrnaseq/
• Bioinformatics and Seq courses: http://www.isb-sib.ch/training/training-activities-schedule/archive-2013.html
• Variant Detection (Model organism) Advanced tutorial https://docs.google.com/document/pub?id=1CuKkKylVDb03tnN7RSWl5EUzleetn0ctjmvaidPKLxM
• Variant Detection Introductory tutorial https://docs.google.com/document/pub?id=1ZRzrjjOCvtAu3m-IKL-rbJ1f4On60dDL_IEwG7oejdI
• Microbial de novo Assembly for Illumina Data Introductory tutorial https://docs.google.com/document/pub?id=1N3AB9ptISUu4zULqe1kXpVF0BDyGb5f5yzxWSJd_WNM
• RNAseq Differential Gene Expression Introductory tutorial https://docs.google.com/document/pub?id=1KbTiBHtvHLfPRZ39AY3uriazrINA8TJzgjjwn1zPP7Y

" Please add your favourite NGS link below in comment section for the benefit of bioinformatics community ". 

Address of the bookmark: http://chagall.med.cornell.edu/NGScourse/

More at http://busco.ezlab.org/

Address of the bookmark: http://busco.ezlab.org/

Address of the bookmark: https://urgi.versailles.inra.fr/Tools/REPET/TEannot-tuto

TULIP currently consists of to Perl scripts, tulipseed.perl and tulipbulb.perl. These are very much intended as prototypes, and additional components and/or implementations are likely to follow. 
Tulipseed takes as input alignments files of long reads to sparse short seeds, and outputs a graph and scaffold structures. Tulipbulb adds long read sequencing data to these.

https://github.com/Generade-nl/TULIP

Address of the bookmark: https://github.com/Generade-nl/TULIP

Identify pairs of contigs that are syntenic and move one of them to the haplotig 'pool'. The pipeline uses mapped read coverage and Minimap2 alignments to determine which contigs to keep for the haploid assembly. Dotplots are optionally produced for all flagged contig matches, juxtaposed with read-coverage, to help the user determine the proper assignment of any remaining ambiguous contigs. The pipeline will run on either a haploid assembly (i.e. Canu, FALCON or FALCON-Unzip primary contigs) or on a phased-diploid assembly (i.e. FALCON-Unzip primary contigs + haplotigs). Here are two examples of how Purge Haplotigs can improve a haploid and diploid assembly.

Address of the bookmark: https://bitbucket.org/mroachawri/purge_haplotigs

Address of the bookmark: https://github.com/envgen

Pipeline flowchart:

Address of the bookmark: https://github.com/Tom-Jenkins/nextflow-pipelines/blob/main/docs/variant-calling.md