MethylDackel
A (mostly) universal methylation extractor for BS-seq experiments. (by dpryan79)
MMseqs2
MMseqs2: ultra fast and sensitive search and clustering suite (by soedinglab)
| MethylDackel | MMseqs2 | |
|---|---|---|
| 1 | 4 | |
| 153 | 1,265 | |
| - | 2.4% | |
| 3.0 | 7.7 | |
| 3 months ago | 15 days ago | |
| C | C | |
| MIT License | GNU General Public License v3.0 only |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
MethylDackel
Posts with mentions or reviews of MethylDackel.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-06-08.
-
WGBS normalization methods inquiry
Anyway, I'm curious if there are methods used to normalize the data to account for the difference in sequencing depth. I've already employed random subsampling like they used in the EpiQC study to achieve a similar mean coverage across the board, but I'm curious if there are any other known methods which minimize information loss. For context, I won't be doing DML/DMR detection (I know these methods can take coverage into account when merging DMRs and performing statistical analyses), but rather tissue of origin estimation between sets. My data are per-CpG context bedGraph files from MethylDackel, but I have access to the per-Cytosine bedGraphs all the way up to the raw FASTQ files.
MMseqs2
Posts with mentions or reviews of MMseqs2.
We have used some of these posts to build our list of alternatives
and similar projects.
-
Clustering tool that could help cluster protein sequences based on percentage identity
A tool I often recommend for sequence clustering is mmseqs2 : https://github.com/soedinglab/MMseqs2, fast and efficient :)
- MMseqs2 – an example of great software for biology
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Metagenomics: abundances of short reads using genome databases
Tools like the the mmseqs2 "taxonomy" module, or DIAMOND v2, can efficiently align contigs to genome databases to assign taxonomy, but it seems like they aren't intended to provide abundance estimates for each taxon (since that would require mapping reads, and mmseqs2 can't even use paired-reads). Can anyone recommend tools or methods for A) connecting per-contig coverage information to contig taxonomy, or B) mapping short reads against genome databases?
- Retrieving One-to-One Orthologs of Unprocessed cDNAs
What are some alternatives?
When comparing MethylDackel and MMseqs2 you can also consider the following projects:
minimap2 - A versatile pairwise aligner for genomic and spliced nucleotide sequences
kraken-biom - Create BIOM-format tables (http://biom-format.org) from Kraken output (http://ccb.jhu.edu/software/kraken/, https://github.com/DerrickWood/kraken).
bwa - Burrow-Wheeler Aligner for short-read alignment (see minimap2 for long-read alignment)
samtools - Tools (written in C using htslib) for manipulating next-generation sequencing data