Bioconda version

[sorenwacker]

Hi,

we were trying to install the software, but did not succeed so far. Would it be possible for you to give more instructions on the README? It would also be great to have a bioconda recipe available in the bioconda channel. It would be good to add a GitHub action that tests the installation. It would also serve as a good demonstration for how to install the software. Often there are hidden dependencies, using Actions would always start from a fresh system and reveal such dependencies.

[Xuuuurj]

Hi @sorenwacker ,

Recently, I installed the software successfully. I hope this can help you.

conda create -n meshclust
conda activate meshclust
conda install -c conda-forge gcc=8.5.0
conda install -c conda-forge cmake
conda install -c conda-forge make

git clone https://github.com/BioinformaticsToolsmith/Identity.git
cd Identity/
mkdir bin
cd bin
cmake ..
make

meshclust -h

(meshclust) [xuuuurj@head01 meshclust]$ conda list
# packages in environment at /data/home/xuuuurj/miniconda3/envs/meshclust:
#
# Name                    Version                   Build  Channel
_libgcc_mutex             0.1                 conda_forge    conda-forge
_openmp_mutex             4.5                       2_gnu    conda-forge
binutils_impl_linux-64    2.43                 h4bf12b8_4    conda-forge
bzip2                     1.0.8                h4bc722e_7    conda-forge
c-ares                    1.34.5               hb9d3cd8_0    conda-forge
ca-certificates           2025.4.26            hbd8a1cb_0    conda-forge
cmake                     4.0.2                h74e3db0_0    conda-forge
gcc                       8.5.0               h143be6b_10    conda-forge
gcc_impl_linux-64         8.5.0               hef569ad_19    conda-forge
kernel-headers_linux-64   3.10.0              he073ed8_18    conda-forge
keyutils                  1.6.1                h166bdaf_0    conda-forge
krb5                      1.21.3               h659f571_0    conda-forge
ld_impl_linux-64          2.43                 h712a8e2_4    conda-forge
libcurl                   8.14.1               h332b0f4_0    conda-forge
libedit                   3.1.20250104    pl5321h7949ede_0    conda-forge
libev                     4.33                 hd590300_2    conda-forge
libexpat                  2.7.0                h5888daf_0    conda-forge
libgcc                    15.1.0               h767d61c_2    conda-forge
libgcc-devel_linux-64     8.5.0               ha5d7ed8_19    conda-forge
libgcc-ng                 15.1.0               h69a702a_2    conda-forge
libgomp                   15.1.0               h767d61c_2    conda-forge
liblzma                   5.8.1                hb9d3cd8_1    conda-forge
libnghttp2                1.64.0               h161d5f1_0    conda-forge
libsanitizer              8.5.0               hb70a093_19    conda-forge
libssh2                   1.11.1               hcf80075_0    conda-forge
libstdcxx                 15.1.0               h8f9b012_2    conda-forge
libstdcxx-ng              15.1.0               h4852527_2    conda-forge
libuv                     1.51.0               hb9d3cd8_0    conda-forge
libzlib                   1.3.1                hb9d3cd8_2    conda-forge
make                      4.4.1                hb9d3cd8_2    conda-forge
ncurses                   6.5                  h2d0b736_3    conda-forge
openssl                   3.5.0                h7b32b05_1    conda-forge
rhash                     1.4.6                hb9d3cd8_1    conda-forge
sysroot_linux-64          2.17                h0157908_18    conda-forge
tzdata                    2025b                h78e105d_0    conda-forge
zstd                      1.5.7                hb8e6e7a_2    conda-forge

This is the log file generated by using this software.

(meshclust) [xuuuurj@head01 output]$ cat meshclust.out

MeShClust 2.0 is developed by Hani Z. Girgis, PhD.

This program clusters DNA sequences using identity scores obtained without alignment.

Copyright (C) 2021-2022 Hani Z. Girgis, PhD

Academic use: Affero General Public License version 1.

Any restrictions to use for profit or non-academics: Alternative commercial license is required.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

Please contact Dr. Hani Z. Girgis (hzgirgis@buffalo.edu) if you need more information.

Please cite the following papers:
        MeShClust v3.0: High-quality clustering of DNA sequences using the mean shift algorithm
        and alignment-free identity scores (2022). Hani Z. Girgis, BMC Genomics, 23(1):423.

        Identity: Rapid alignment-free prediction of sequence alignment identity scores using
        self-supervised general linear models (2021). Hani Z. Girgis, Benjamin T. James, and
        Brian B. Luczak. NAR Genom Bioinform, 13(1), lqab001.

        A survey and evaluations of histogram-based statistics in alignment-free sequence
        comparison (2019). Brian B. Luczak, Benjamin T. James, and Hani Z. Girgis. Briefings
        in Bioinformatics, 20(4):1222–1237.

        MeShClust: An intelligent tool for clustering DNA sequences (2018). Benjamin T. James,
        Brian B. Luczak, and Hani Z. Girgis. Nucleic Acids Res, 46(14):e83.

Database file: test.fa
Output file: output.txt
Cores: 50
Provided threshold: 0.8
Block size for all vs. all: 25000
Block size for reading sequences: 100000
Number of data passes: 10
Can assign all: No


Average: 10275
K: 6
Histogram size: 4096
A histogram entry is 32 bits.
Generating data.
Preparing data ...
        Positive examples: 10000
        Training size: 5000
        Validation size: 5000
Better performance of: 0.000414404
        jeffrey_divergence
Better performance of: 0.000211504
        jeffrey_divergence
        simMM x d2_s_r
Better performance of: 0.000175419
        jeffrey_divergence
        minkowski x covariance_r
        cosine x d2_s_r
        simMM x d2_s_r
Better performance of: 0.000151581
        squared_chord
        jeffrey_divergence
        minkowski x covariance_r
        cosine x d2_s_r
        covariance_r x sim_ratio
        simMM x d2_s_r
        chebyshev^2 x minkowski^2
Selected statistics:
        squared_chord
        jeffrey_divergence
        minkowski x covariance_r
        cosine x d2_s_r
        covariance_r x sim_ratio
        simMM x d2_s_r
        chebyshev^2 x minkowski^2
Finished training.
        MAE: 0.00817392
        MSE: 0.000151581
Optimizing ...
Validating ...
        MAE: 0.00993338
        MSE: 0.000208159

Clustering ...

Data run 1 ...
        Processed sequences: 3260
        Unprocessed sequences: 0
        Found centers: 11

Assigning ...
Finished.

Thanks for using MeShClust v2.0. Please post any questions or problems on GitHub:
https://github.com/BioinformaticsToolsmith/Identity or email Dr. Hani Z. Girgis.

Best regards,
Rongjing