hh-suite
Remote protein homology detection suite. (by soedinglab)
MethylDackel
A (mostly) universal methylation extractor for BS-seq experiments. (by dpryan79)
hh-suite | MethylDackel | |
---|---|---|
2 | 1 | |
499 | 152 | |
1.8% | - | |
0.0 | 3.0 | |
9 months ago | 3 months ago | |
C | C | |
GNU General Public License v3.0 only | MIT License |
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.
hh-suite
Posts with mentions or reviews of hh-suite.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-03-02.
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.
What are some alternatives?
When comparing hh-suite and MethylDackel you can also consider the following projects:
MMseqs2 - MMseqs2: ultra fast and sensitive search and clustering suite
minimap2 - A versatile pairwise aligner for genomic and spliced nucleotide sequences
foldseek - Foldseek enables fast and sensitive comparisons of large structure sets.
bwa - Burrow-Wheeler Aligner for short-read alignment (see minimap2 for long-read alignment)
direct-io - Direct IO helpers for block devices and regular files on FreeBSD, Linux, macOS and Windows.
seqtk - Toolkit for processing sequences in FASTA/Q formats
cglm - 📽 Highly Optimized 2D / 3D Graphics Math (glm) for C
bioawk - BWK awk modified for biological data
celfie - cfDNA cell type of origin estimation