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

Abstract: Strand NGS includes comprehensive workflows for DNA-Seq, RNA-Seq, Small RNA-Seq, ChIP-Seq, MeDIP-Seq, and Methyl-Seq analysis. Each workflow includes a quality assessment and filter section, followed by a workflow-specific analysis section. The pipeline functionality in Strand NGS allows users to execute a sequence of analysis steps with specific parameters - all without any manual intervention. This simplifies the analysis in large scale sequencing projects where every sample needs to be processed identically.

In this webinar we will discuss the pre-packaged pipelines present in Strand NGS. The packaged pipelines have well-chosen default parameters and are suitable for users analyzing data for the first time in the tool. We will also show how advanced users can customize pipelines and share them with other Strand NGS users. Finally, we will show a brief glimpse of an elaborate pipeline that aligns reads, filters poor-quality matches, computes coverage metrics, identifies variants, checks for sample cross-contamination, and emails quality reports - all from within Strand NGS.

Speaker: Dr. Vamsi Veeramachaneni, Vice President - Bioinformatics, Strand Life Sciences

Details: Session 1: 2:30 PM IST, Session 2 : 10:30 PM IST
Register here: http://www.strand-ngs.com/webinar_registration

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

1. Reducing time in analysis and increasing accuracy of results by only considering those reads which should assemble together.
2. Reducing/removing reference bias as compared to mapping based approaches.

The software is designed to work in situations where a whole-genome assembly is not the objective, but rather when the researcher wishes to assemble discreet 'targets' contained within next-generation shotgun sequence data. ARC decomplexifies the traditionally difficult problem of assembly by breaking the reads into small, manageable subsets which can then be assembled quickly and efficiently in parallel. Applications include those in which the researcher wishes to de novo assemble specific content and a set of semi-similar reference targets is available to initialize the assembly process.

https://ibest.github.io/ARC/

Address of the bookmark: https://ibest.github.io/ARC/

https://www.nature.com/articles/nm.3975

Address of the bookmark: https://www.nature.com/articles/nm.3975

HaploTypo is implemented in Python 2.7 and Python 3.5, and is freely available at https://github.com/gabaldonlab/haplotypo, and as a Docker image.

Address of the bookmark: https://github.com/gabaldonlab/haplotypo

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6733440/

Address of the bookmark: https://github.com/kammoji/gapFinisher

1. Co-assembly procedure with read mapping for estimation of the abundances of genes in each metagenome
2. Co-assembly of a large number of metagenomes via merging of individual metagenomes
3. Includes binning and bin checking, for retrieving individual genomes
4. The results are stored in a database, where they can be easily exported and shared, and can be inspected anywhere using a web interface.
5. Internal checks for the assembly and binning steps inform about the consistency of contigs and bins, allowing to spot potential chimeras.
6. Metatranscriptomic support via mapping of cDNA reads against reference metagenomes

Address of the bookmark: https://github.com/jtamames/SqueezeMeta