List of analysis tools developed for Oxford Nanopore data

BWA
Fast nanopore data tuned alignment tool
https://github.com/lh3/bwa

GraphMap
Mapper for long and error-prone reads
https://github.com/isovic/graphmap

LAST
Nanopore tuned alignment tool
http://last.cbrc.jp/

LINKS
Software tool for long read scaffolding
https://github.com/warrenlr/LINKS/

marginAlign
Tools to align nanopore reads to a reference
https://github.com/benedictpaten/marginAlign

minoTour
Real time analysis tools
http://minotour.nottingham.ac.uk/

nanoCORR
Error-correction tool for nanopore sequence data
https://github.com/jgurtowski/nanocorr

NanoOK
Software for nanopore data, quality and error profiles
https://documentation.tgac.ac.uk/display/NANOOK/NanoOK

Nanopolish
Nanopore analysis and genome assembly software
https://github.com/jts/nanopolish

nanopore
Variant-detection tool for nanopore sequence data
https://github.com/mitenjain/nanopore

Nanocorrect
Error-correction tool for nanopore sequence data
https://github.com/jts/nanocorrect/

npReader
Real-time conversion and analysis of nanopore reads
https://github.com/mdcao/npReader

poRe
Tool for analyzing and visualizing nanopore data
https://sourceforge.net/p/rpore/wiki/Home/

PoreSeq
Error-correction and variant-calling software
https://github.com/tszalay/poreseq

Poretools
Nanopore sequence analysis and visualization software
https://github.com/arq5x/poretools

SSPACE-LongRead
Genome scaffolding tool
http://www.baseclear.com/genomics/bioinformatics/basetools/SSPACE-longread

SMIS
Genome scaffolding tool
https://sourceforge.net/projects/phusion2/files/smis/

List of assemblers for Oxford Nanopore MinION long reads

LQS
DALIGNER, Celera OLC Nanocorrect,
Nanopolish corrector
https://github.com/jts/nanopolish

PBcR
HGAP or BLASR, Celera OLC
PBcR corrector
http://wgs-assembler.sourceforge.net/wiki/index.php/PBcR
–
Canu
MHAP, Celera OLC
Canu corrector
https://github.com/marbl/canu

Falcon
String graph, Celera OLC
Falcon corrector
https://github.com/PacificBiosciences/falcon

Miniasm
OLC
https://github.com/lh3/miniasm

ra-integrate
OLC
https://github.com/mariokostelac/ra-integrate/

ALLPATHS-LG
de Bruijn graph
ALLPATHS-L corrector
https://www.broadinstitute.org/software/allpaths-lg/blog/?page_id=12

SPAdes
de Bruijn graph
SPAdes corrector
http://bioinf.spbau.ru/spades

Genome assembly projects typically run multiple algorithms in an attempt to find the single best assembly, although those assemblies often have complementary, if untapped, strengths and weaknesses. We present our metassembler algorithm that merges multiple assemblies of a genome into a single superior sequence. 

Address of the bookmark: https://sourceforge.net/projects/metassembler/?source=directory

It installs in any computer even if you don't have administrator rights!

Address of the bookmark: http://www.dnabaser.com/download/DNA-Counter/index.html

Computational methods used by the Shasta assembler include:

• Using a run-length representation of the read sequence. This makes the assembly process more resilient to errors in homopolymer repeat counts, which are the most common type of errors in Oxford Nanopore reads.
• Using in some phases of the computation a representation of the read sequence based on markers, a fixed subset of short k-mers (k ≈ 10).

More at https://chanzuckerberg.github.io/shasta/index.html

Address of the bookmark: https://github.com/chanzuckerberg/shasta

https://academic.oup.com/bioinformatics/article/35/12/2153/5162749

Address of the bookmark: https://github.com/arzwa/wgd

Breeding Insight is funded by the U.S. Department of Agriculture (USDA) Agricultural Research Service (ARS) through Cornell University. The USDA ARS delivers scientific solutions to national and global agricultural challenges. As a global leader in agricultural discovery through scientific excellence, ARS is committed to delivering cutting-edge, scientific tools and innovative solutions for American farmers, producers, industry, and communities to support the nourishment and well-being of all people; sustaining our nation’s agroecosystems and natural resources; and ensuring the economic competitiveness and excellence of our agriculture.

Address of the bookmark: https://www.breedinginsight.org/

• Homology-based misassembly correction
• Homology-based assembly scaffolding and patching
• Scaffold merging

Address of the bookmark: https://github.com/malonge/RagTag

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.

HECIL’s core algorithm by introducing an iterative learning paradigm that enhances the correction policy at each iteration by incorporating knowledge gathered from previous iterations via data-driven confidence metrics assigned to prior corrections.

Address of the bookmark: https://github.com/NDBL/HECIL

1) expression-based heat maps from transcriptomic, proteomic and metabolomic experiments; 2) pairwise distance maps;

3) correlation maps;

4) image overlay heat maps;

5) latitude and longitude heat maps and

6) geopolitical (choropleth) heat maps.

Heatmapper offers a number of simple and intuitive customization options for easy adjustments to each heat map’s appearance and plotting parameters. Heatmapper also allows users to interactively explore their numeric data values by hovering their cursor over each heat map, or by using a searchable/sortable data table view.

Ref https://www.ncbi.nlm.nih.gov/pubmed/27190236

Address of the bookmark: http://www2.heatmapper.ca/