| Name | kmer-map JSON |
| Version |
0.0.7
JSON |
| download |
| home_page | None |
| Summary | A package for visualizing kmers. |
| upload_time | 2024-10-04 21:29:58 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. 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Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. 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You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS Copyright [2024] Chengbo Fu, Lu Cheng, Contact: lu.cheng.ac@gmail.com Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. |
| keywords |
kmap
kmer
visualization
dimensionality reduction
dna
sequences
umap
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
taichi
kmer-map
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Kmer Manifold Approximation and Projection (KMAP)
kmap is a package for visualizing kmers in 2D space.
-
## Installation
```
conda create --name=kmap_test python=3.11
conda activate kmap_test
conda install anaconda::scipy
conda install anaconda::numpy
conda install anaconda::matplotlib
conda install anaconda::pandas
conda install anaconda::click
conda install anaconda::tomli-w
conda install anaconda::requests
conda install conda-forge::biopython
conda install conda-forge::networkxx
conda install bioconda::logomaker
pip install taichi
pip install kmer-map
```
OR
```Mac
# Mac
conda env create -f environment.yml
conda activate kmap_test
```
OR
```Linux
# Linux
conda env create -f env.yml
conda activate kmap_test
pip install -r requirements.txt
```
## Example usage
### General workflow
The following code shows the typical workflow of the test data in `./tests/test.fa`
```bash
# step 0: preprocess input fast file
kmap preproc --fasta_file ./tests/test.fa --res_dir ./test
# step 1: scanning for motifs
kmap scan_motif --res_dir ./test --debug true
# step 2: visualize kmers
# now edit the "./test/config.toml" file, in the 3rd section "visualization"
# change "n_max_iter = 2500" to "n_max_iter = 100"
kmap visualize_kmers --res_dir ./test --debug True
```
### Detailed workflow
- **Step 0**: confirm kmap is successfully installed
```bash
kmap --help
```
- **Step 1**: create a directory `test` and save the test fasta file [test.fa](./tests/test.fa) in this directory
```bash
mkdir ./test
```
The output folder looks like
```
test
| -- test.fa
```
- **Step 2**: preprocess input fast file
```bash
kmap preproc --fasta_file ./test/test.fa --res_dir ./test
```
The output folder looks like
```
test
| -- test.fa
| -- input.bin.pkl
| -- input.seqboarder.bin.pkl
| -- config.toml
| -- motif_def_table.csv
```
`input.bin.pkl` is the processed `.pkl` file of the input fasta file `test.fa`, which can be read by the pickle module of python.
`input.seqboarder.bin.pkl` is `n_seq x 2` numpy matrix, where the rows index each sequence, and the columns are start and end of each sequence.
`config.toml` contains all the kmap parameters for this analysis task. You can modify the parameters according to your own needs.
`motif_def_table.csv` is the motif definition table, which specify the parameters of the Hamming balls. You can modify the parameters according to your own needs.
- **Step 3**: scan for motifs
```bash
kmap scan_motif --res_dir ./test --debug true
```
The `--debug` option can be omitted. The output folder with file/folder descriptions:
```
test
| -- test.fa
| -- input.bin.pkl
| -- input.seqboarder.bin.pkl
| -- config.toml
| -- motif_def_table.csv
| -- kmer_count[folder]: kmer counts of different kmer lengths, motif occurence file for candidate consensus sequences
| -- candidate_conseq.csv: consensus sequences of significant Hamming balls of diferent kmer length
| -- final_conseq.txt: merged consensus seqences of different kmer lengths, final motifs (2 motif in this case)
| -- final_conseq.info.csv: contain meta information of final consensensus sequences
| -- final.motif_occurence.csv: positions of final conseqs in the input reads
| -- conseq_similarity[folder]: files illustrate similarities between final conseqs
| -- hamming_balls[folder]: Count matrix (calculated from Hamming balls) and logos of final motifs
| -- co_occurence[folder]: files about co-occurence of final conseqs, e.g., co-occurence frequency, distance distribution between different conseqs
| -- motif_pos_density[folder]: motif kmer postion distribution on input sequences
| -- motif_pos_density.np.pkl: numpy array of the position densities
| -- sample_kmers.pkl: sampled kmers for visualization
| -- sample_kmers.tsv: sampled kmers for visualization, second column is final motif label, largest label (2) means random kmers
| -- sample_kmer_hamdist_mat.pkl: Hamming distance matrix of sampled kmers, used as input for kmer visualization
```
- **Step 4**: visualize kmers
Now now edit the `./test/config.toml` file,
in the 3rd section `visualization` change `n_max_iter = 2500` to `n_max_iter = 100`.
This will reduce the optimization steps from 2500 to 100, which saves lots of running time.
```bash
kmap visualize_kmers --res_dir ./test --debug True
```
This will generate three additional files in the output directory
```
test
| -- low_dim_data.tsv: low dimensional embeddings, the columns are (x, y, motif_label), where the largest label is random kmers.
| -- ld_data.pdf: 2d plot of the embeddings in pdf
| -- ld_data.png: 2d plot of the embeddings in png
```
### Auxiliary functions
Sometimes we may want to adjust the default workflow.
For example, after checking the motif logos in the `hamming_balls` directory, we feel the consensus is `AATCGATAGC`, instead of `A[AATCGATAGC]GA`.
We can re-generate the motif use the following commands.
```bash
kmap ex_hamball --res_dir ./test --conseq AATCGATAGC --return_type matrix --output_file ./test/hamming_balls/AATCGATAGC_cntmat.csv
kmap draw_logo --cnt_mat_numpy_file ./test/hamming_balls/AATCGATAGC_cntmat.csv --output_fig_file ./test/hamming_balls/logo.pdf
```
The motif logo for this consensus sequence is given by `./test/hamming_balls/logo.pdf`
For example, we feel the maximum number of mutations `max_ham_dist=5` for the consensus sequence `GTACGTAGGTCCTA` (defined in `motif_def_table.csv`, k=14) is too large.
We want to change the number of maximum mutations to 3. We can derive the new motif based on `max_ham_dist=5` using the following commands.
```bash
kmap ex_hamball --res_dir ./test --conseq GTACGTAGGTCCTA --return_type matrix --max_ham_dist 3 --output_file ./test/hamming_balls/GTACGTAGGTCCTA_cntmat.csv
kmap draw_logo --cnt_mat_numpy_file ./test/hamming_balls/GTACGTAGGTCCTA_cntmat.csv --output_fig_file ./test/hamming_balls/logo.pdf
```
The motif logo for this consensus sequence is given by `./test/hamming_balls/logo.pdf`
It take efforts to compare the consensus sequences, especially when reverse complements are considered.
We can check the similarities between candidate consensus sequences using the following command.
```bash
kmap align_conseq --conseq_csv_file ./test/candidate_conseq.csv --out_dir ./test/candidate_seq_similarity
# or only final conseqs
kmap align_conseq --conseq_csv_file ./test/ final_conseq.info.csv --out_dir ./test/conseq_similarity
```
A dendrogram file `dendrogram.pdf` will be generated in the output directory `./test/candidate_seq_similarity`.
The dendrogram shows the similarities between all candidate consensus sequences, as well as their reverse complements.
Hierarchical clustering is performed on the dendrogram. For each derived cluster, local pairwise alignment is generated
for each pair of consensus sequences in that cluster.
In Chip-seq data, we generally have the peak bed files, then we extract the corresponding fasta file from these peaks
and perform kmap analysis. After that, we would like to know the actual locations of detected motifs on the reference genome.
We could use the following command:
```bash
cd ./test # change to the result directory
kmap extract_motif_locations --bed_file your_bed_file.bed
```
A new folder `motif_locations` will be generated in the result directory, which contains the actual genomic location of
detected final motifs.
[comment]: <> (Release commands)
[comment]: <> (python -m build)
[comment]: <> (python3 -m twine upload --repository kmer-map dist/*)
Raw data
{
"_id": null,
"home_page": null,
"name": "kmer-map",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.11",
"maintainer_email": null,
"keywords": "kmap, kmer, visualization, dimensionality reduction, DNA, sequences, umap",
"author": null,
"author_email": "\"Chengbo Fu, Lu Cheng.\" <lu.cheng.ac@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/61/ba/687921ea196a54d7490fbdb8fc7067582b4ad6d4052ffa796ab0d67ceb92/kmer_map-0.0.7.tar.gz",
"platform": null,
"description": "# Kmer Manifold Approximation and Projection (KMAP)\nkmap is a package for visualizing kmers in 2D space. \n-\n\n## Installation\n\n```\nconda create --name=kmap_test python=3.11\nconda activate kmap_test\nconda install anaconda::scipy\nconda install anaconda::numpy\nconda install anaconda::matplotlib\nconda install anaconda::pandas\nconda install anaconda::click\nconda install anaconda::tomli-w\nconda install anaconda::requests\nconda install conda-forge::biopython\nconda install conda-forge::networkxx\nconda install bioconda::logomaker\npip install taichi\npip install kmer-map\n```\n\nOR\n\n```Mac\n# Mac\nconda env create -f environment.yml\nconda activate kmap_test\n```\n\nOR\n\n```Linux\n# Linux\nconda env create -f env.yml \nconda activate kmap_test\npip install -r requirements.txt\n```\n\n\n## Example usage\n### General workflow\nThe following code shows the typical workflow of the test data in `./tests/test.fa`\n```bash\n# step 0: preprocess input fast file\nkmap preproc --fasta_file ./tests/test.fa --res_dir ./test \n\n# step 1: scanning for motifs \nkmap scan_motif --res_dir ./test --debug true\n\n# step 2: visualize kmers\n# now edit the \"./test/config.toml\" file, in the 3rd section \"visualization\"\n# change \"n_max_iter = 2500\" to \"n_max_iter = 100\" \nkmap visualize_kmers --res_dir ./test --debug True\n```\n\n\n\n### Detailed workflow\n- **Step 0**: confirm kmap is successfully installed\n```bash\nkmap --help\n```\n- **Step 1**: create a directory `test` and save the test fasta file [test.fa](./tests/test.fa) in this directory\n```bash \nmkdir ./test\n```\nThe output folder looks like\n```\ntest\n | -- test.fa\n```\n- **Step 2**: preprocess input fast file\n```bash \nkmap preproc --fasta_file ./test/test.fa --res_dir ./test\n```\nThe output folder looks like\n```\ntest\n | -- test.fa\n | -- input.bin.pkl\n | -- input.seqboarder.bin.pkl\n | -- config.toml\n | -- motif_def_table.csv\n```\n`input.bin.pkl` is the processed `.pkl` file of the input fasta file `test.fa`, which can be read by the pickle module of python. \n`input.seqboarder.bin.pkl` is `n_seq x 2` numpy matrix, where the rows index each sequence, and the columns are start and end of each sequence. \n`config.toml` contains all the kmap parameters for this analysis task. You can modify the parameters according to your own needs.\n`motif_def_table.csv` is the motif definition table, which specify the parameters of the Hamming balls. You can modify the parameters according to your own needs.\n- **Step 3**: scan for motifs\n```bash \nkmap scan_motif --res_dir ./test --debug true \n```\nThe `--debug` option can be omitted. The output folder with file/folder descriptions:\n```\ntest\n | -- test.fa\n | -- input.bin.pkl\n | -- input.seqboarder.bin.pkl\n | -- config.toml\n | -- motif_def_table.csv\n | -- kmer_count[folder]: kmer counts of different kmer lengths, motif occurence file for candidate consensus sequences\n | -- candidate_conseq.csv: consensus sequences of significant Hamming balls of diferent kmer length\n | -- final_conseq.txt: merged consensus seqences of different kmer lengths, final motifs (2 motif in this case)\n | -- final_conseq.info.csv: contain meta information of final consensensus sequences\n | -- final.motif_occurence.csv: positions of final conseqs in the input reads \n | -- conseq_similarity[folder]: files illustrate similarities between final conseqs\n | -- hamming_balls[folder]: Count matrix (calculated from Hamming balls) and logos of final motifs\n | -- co_occurence[folder]: files about co-occurence of final conseqs, e.g., co-occurence frequency, distance distribution between different conseqs\n | -- motif_pos_density[folder]: motif kmer postion distribution on input sequences\n | -- motif_pos_density.np.pkl: numpy array of the position densities\n | -- sample_kmers.pkl: sampled kmers for visualization\n | -- sample_kmers.tsv: sampled kmers for visualization, second column is final motif label, largest label (2) means random kmers\n | -- sample_kmer_hamdist_mat.pkl: Hamming distance matrix of sampled kmers, used as input for kmer visualization\n```\n- **Step 4**: visualize kmers\nNow now edit the `./test/config.toml` file, \nin the 3rd section `visualization` change `n_max_iter = 2500` to `n_max_iter = 100`. \nThis will reduce the optimization steps from 2500 to 100, which saves lots of running time.\n```bash \nkmap visualize_kmers --res_dir ./test --debug True\n```\nThis will generate three additional files in the output directory\n```\ntest\n | -- low_dim_data.tsv: low dimensional embeddings, the columns are (x, y, motif_label), where the largest label is random kmers.\n | -- ld_data.pdf: 2d plot of the embeddings in pdf \n | -- ld_data.png: 2d plot of the embeddings in png\n```\n\n### Auxiliary functions\n\nSometimes we may want to adjust the default workflow.\nFor example, after checking the motif logos in the `hamming_balls` directory, we feel the consensus is `AATCGATAGC`, instead of `A[AATCGATAGC]GA`. \nWe can re-generate the motif use the following commands.\n```bash\nkmap ex_hamball --res_dir ./test --conseq AATCGATAGC --return_type matrix --output_file ./test/hamming_balls/AATCGATAGC_cntmat.csv \nkmap draw_logo --cnt_mat_numpy_file ./test/hamming_balls/AATCGATAGC_cntmat.csv --output_fig_file ./test/hamming_balls/logo.pdf\n```\nThe motif logo for this consensus sequence is given by `./test/hamming_balls/logo.pdf`\n\nFor example, we feel the maximum number of mutations `max_ham_dist=5` for the consensus sequence `GTACGTAGGTCCTA` (defined in `motif_def_table.csv`, k=14) is too large. \nWe want to change the number of maximum mutations to 3. We can derive the new motif based on `max_ham_dist=5` using the following commands.\n```bash\nkmap ex_hamball --res_dir ./test --conseq GTACGTAGGTCCTA --return_type matrix --max_ham_dist 3 --output_file ./test/hamming_balls/GTACGTAGGTCCTA_cntmat.csv \nkmap draw_logo --cnt_mat_numpy_file ./test/hamming_balls/GTACGTAGGTCCTA_cntmat.csv --output_fig_file ./test/hamming_balls/logo.pdf\n```\nThe motif logo for this consensus sequence is given by `./test/hamming_balls/logo.pdf`\n\nIt take efforts to compare the consensus sequences, especially when reverse complements are considered.\nWe can check the similarities between candidate consensus sequences using the following command.\n```bash\nkmap align_conseq --conseq_csv_file ./test/candidate_conseq.csv --out_dir ./test/candidate_seq_similarity\n# or only final conseqs\nkmap align_conseq --conseq_csv_file ./test/ final_conseq.info.csv --out_dir ./test/conseq_similarity\n```\nA dendrogram file `dendrogram.pdf` will be generated in the output directory `./test/candidate_seq_similarity`. \nThe dendrogram shows the similarities between all candidate consensus sequences, as well as their reverse complements.\nHierarchical clustering is performed on the dendrogram. For each derived cluster, local pairwise alignment is generated\nfor each pair of consensus sequences in that cluster.\n\nIn Chip-seq data, we generally have the peak bed files, then we extract the corresponding fasta file from these peaks\nand perform kmap analysis. After that, we would like to know the actual locations of detected motifs on the reference genome.\nWe could use the following command:\n```bash\ncd ./test # change to the result directory\nkmap extract_motif_locations --bed_file your_bed_file.bed \n```\nA new folder `motif_locations` will be generated in the result directory, which contains the actual genomic location of\ndetected final motifs.\n\n[comment]: <> (Release commands)\n[comment]: <> (python -m build) \n[comment]: <> (python3 -m twine upload --repository kmer-map dist/*)\n\n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\" \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. 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