Address of the bookmark: https://github.com/AppliedBioinformatics/RefKA

1 Falcon https://github.com/PacificBiosciences/pb-assembly

2 Canu assembler http://canu.readthedocs.io/en/latest/index.html

3 Miniasm assembler https://github.com/lh3/miniasm

4 PBJelly scaffolding tool https://sourceforge.net/projects/pb-jelly/

5 ARCS scaffolding tool https://github.com/bcgsc/arcs

6 Redundans reduction and scaffolding tool https://github.com/Gabaldonlab/redundans

7 Arrow error correction https://github.com/PacificBiosciences/ GenomicConsensus

8 PILON error correction https://github.com/broadinstitute/pilon/wiki

9 BUSCO single copy gene markers http://busco.ezlab.org/

10 Bandage graph assembly viewer https://rrwick.github.io/Bandage/

11 Gepard dotter http://cube.univie.ac.at/gepard

12 MUMmer aligner and plotter http://mummer.sourceforge.net/

https://academic.oup.com/nar/article/47/11/e63/5377471

Address of the bookmark: https://github.com/linzhi2013/MitoZ

1. SPAdes: An assembler specifically designed for single-cell and multi-cell bacterial genomes, as well as small eukaryotic genomes.

2. ABySS: A parallelized assembler for large genomes that uses de Bruijn graphs.

3. Velvet: Another de Bruijn graph-based assembler optimized for short-read sequencing data.

4. SOAPdenovo: A de Bruijn graph-based assembler designed for short reads, widely used for assembling large and complex genomes.

5. MaSuRCA: A hybrid assembler that combines data from multiple sequencing technologies, such as Illumina and PacBio.

6. Canu: A long-read assembler optimized for PacBio and Oxford Nanopore sequencing data.

7. Flye: A long-read assembler suitable for bacterial and small eukaryotic genomes.

8. SMARTdenovo: An assembler designed for long reads, particularly suited for PacBio data.

9. SPAdes Long Read (SPAdesLR): An extension of SPAdes for long-read data, such as those from PacBio or Nanopore.

10. Minia: An assembler optimized for low memory consumption, suitable for small and medium-sized genomes.

11. Unicycler: A hybrid assembler that combines short and long reads for circular bacterial genome assembly.

12. wtdbg2: A de Bruijn graph assembler for long reads, efficient for very large genomes.

13. Shasta: A long-read assembler that uses the Overlap-Layout-Consensus approach, suitable for PacBio and Nanopore data.

14. Sparc: An assembler designed to handle noisy long reads from Nanopore sequencing.

15. CANA: An assembler for metagenomic data, particularly for complex and diverse microbial communities.

16. Ra Assembler: A metagenome assembler for long reads, designed for highly complex metagenomic samples.

Please note that the field of bioinformatics is constantly evolving, and new assembly tools may have emerged since my last update. Additionally, the performance of these tools can vary depending on the characteristics of the sequencing data and the genome being assembled. When selecting an assembly tool, consider the specific requirements of your project, the available data types, and the computational resources at your disposal. Always refer to the respective tool's documentation and publications for the most up-to-date information and recommendations.

• AssemblyMapper : reference-guided genome assembly
• GapFiller : long-reads based gap filling
• TeloExplorer : telomere identification
• CentroMiner : centromere candidate prediction

https://academic.oup.com/hr/article/10/8/uhad127/7197191?login=false 

Address of the bookmark: http://www.atcgn.com:8080/quarTeT/home.html

More at http://www.yandell-lab.org/software/maker.html

Address of the bookmark: http://www.yandell-lab.org/software/maker.html

The pipeline consists of three steps/modules:

• redundancy reduction: detection and selectively removal of redundant contigs from an initial de novo assembly
• scaffolding: joining of genome fragments using paired-end and/or mate-pairs reads
• gap closing

Redundans is:

• fast & lightweight, multi-core support and memory-optimised, so it can be run even on the laptop for small-to-medium size genomes
• flexible toward many sequencing technologies (Illumina, 454 or Sanger) and library types (paired-end, mate pairs, fosmids)
• modular: every step can be ommited or replaced by another tools

Address of the bookmark: https://github.com/Gabaldonlab/redundans

• velvet (velveth velvetg should be in your PATH)
• R (with Sweave)
• pdflatex (usually part of TeTeX)
• ggplot2 (from R prompt type install.packages("ggplot2","proto","xtable"))
• Perl

Optional:

• BLAT or BLAST (to generate alignments against a reference genome). If using BLAT, add faToTwoBit,gfClient,gfServer to your PATH. If using BLAST, add blastall and formatdb.

Edit permute.sh to your liking, paying particular attention to the kmer, cvCut, expCov, and other flags

To Run:

1. perl fastaAllSize mysequences.fa > mysequences.stat or gunzip -c mysequences.fa.gz | fastaAllSize > mysequences.stat Substitute fastqAllSize for fastq files.
2. ./permute.sh mysequences (leave out the .fa)

https://github.com/leipzig/standardized-velvet-assembly-report

Address of the bookmark: https://github.com/leipzig/standardized-velvet-assembly-report

The arguments accepted by ScaffMatch are:

  -w) Working directory -- this is the directory where ScaffMatch files are stored. These are .sam files produced after mapping reads to contigs and the resulting scaffolds file `scaffolds.fa` fasta file;

  -c) Contig fasta file;

  -m) Command line argument with no options. It is used when .sam files are used instead of reads .fastq files. Do not use this option if you provide reads files;

  -1) (Comma separated list of) either .fastq or .sam file(s) corresponding to the first read of the read pair;

  -2) (Comma separated list of) either .fastq or .sam file(s) corresponding to the second read of the read pair;

  -i) (Comma separated list of) insert size(s) of the library(-ies);

  -s) (Comma separated list of) library(-ies) standard deviation(s) of insert size(s);

  -t) Bundle threshold. Pairs of contigs supported by number of read pairs less than the value of this argument are discarded. Optional argument, by default it is equal to 5;

  -g) Matching heuristics: use `max_weight` for Maximum Weight Matching heuristics with the Insertion step, use `backbone` for Maximum Weight Matching heuristics without the Insertion step, use `greedy` for Greedy Matching heuristics;

  -l) Log file - where to store the logs. Optional argument. By default, stdout is used.

Address of the bookmark: http://alan.cs.gsu.edu/NGS/?q=content/scaffmatch

PEAR evaluates all possible paired-end read overlaps and without requiring the target fragment size as input. In addition, it implements a statistical test for minimizing false-positive results. Together with a highly optimized implementation, it can merge millions of paired end reads within a couple of minutes on a standard desktop computer.

Address of the bookmark: http://sco.h-its.org/exelixis/web/software/pear/doc.html