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CMash-0.3.0
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مشاهده بیشتر
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مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Fast and accurate set similarity estimation via containment min hash (for genomic datasets).
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | CMash-0.3.0 |
| نام | CMash |
| نسخه کتابخانه | 0.3.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | David Koslicki |
| ایمیل نویسنده | dmkoslicki@gmail.com |
| آدرس صفحه اصلی | https://github.com/dkoslicki/CMash |
| آدرس اینترنتی | https://pypi.org/project/CMash/ |
| مجوز | - |
# CMash
CMash is a fast and accurate way to estimate the similarity of two sets. This is a probabilisitic data analysis approach, and uses containment min hashing. Please see the [associated paper](http://www.biorxiv.org/content/early/2017/09/04/184150) for further details (and please cite if you use it):
>Improving Min Hash via the Containment Index with applications to Metagenomic Analysis
>David Koslicki, Hooman Zabeti
>bioRxiv 184150; doi: https://doi.org/10.1101/184150
## Installation
The easiest way to install this is to use [virtualenv](https://virtualenv.pypa.io/en/stable/):
```bash
virtualenv CMashVE
source CMashVE/bin/activate
pip install -U pip
pip install CMash
```
You can also just use ``pip install CMash`` if you don't want to create a virtual environment.
To get the absolute latest edition of CMash, then you can build from the Github repository via:
```bash
virtualenv CMashVE
source CMashVE/bin/activate
pip install -U pip
git clone https://github.com/dkoslicki/CMash.git
cd CMash
pip install -r requirements.txt
```
Note that the python code in this repository is python2 and python3 compatible, but the dependency ``khmer`` technically requires python3 (but ``khmer`` version ``2.1.1`` runs just fine in python2.)
The external dependency ``pytst`` requires python2, so I'm making this a python2 repository.
## Usage
The basic paradigm is to create a reference/training database, form a sample bloom filter, and then query the database.
#### Forming a reference/training database
Say you have three reference fasta/q file: ``ref1.fa``, ``ref2.fa`` and ``ref3.fa``. In a file (here called ``FileNames.txt``), place the absolute paths pointing to the fasta/q files:
```bash
cat FileNames.txt
# /abs/path/to/ref1.fa
# /abs/path/to/ref2.fa
# /abs/path/to/ref3.fa
```
Then you can create the training database via:
```bash
MakeDNADatabase.py FileNames.txt TrainingDatabase.h5
```
See ``MakeDNADatabase.py -h`` for more options when forming a database.
#### Creating a sample bloom filter
Given a (large) query fasta/q file ``Metagenome.fa``, you can *optionally* create a bloom filter via ``MakeNodeGraph.py Metagenome.fa .``.
See ``MakeNodeGraph.py -h`` for more details about this function.
This step is not strictly necessary (as the next step automatically forms a nodegraph/bloom filter if you didn't already create one).
However, I've provided this script in case you want to pre-process a bunch of metagenomes.
#### Query the database
To get containment and Jaccard index estimates of the references files in your query file ``Metagenome.fa``, use something like the following ``QueryDNADatabase.py Metagenome.fa TrainingDatabase.h5 Output.csv``.
There are a bunch of options available: ``QueryDNADatabase.py -h``. The output file is a CSV file with rows corresponding (in this case) to ``ref1.fa``, ``ref2.fa``, and ``ref3.fa`` and columns corresponding to the containment index estimate, intersection cardinality, and Jaccard index estimate.
#### Other functionality
The module ``MinHash`` (imported in python via ``from CMash import MinHash as MH``) has a bunch more functionality, including (but not limited to!):
1. Fast updates to the training databases (via ``help(MH.delete_from_database)``, ``help(MH.insert_to_database)``, ``help(MH.union_databases)``)
2. Ability to form a matrix of Jaccard indexes (for comparison of all pairwise Jaccard indexes of organisms in the training database). This is useful for identifying redundances/patterns/structure in your training database: ``help(MH.form_jaccard_count_matrix)`` and ``help(MH.form_jaccard_matrix)``.
3. Access to the k-mers that MinHash randomly selected (see the class ``CountEstimator`` and the associated ``_kmers`` data structure.)
I'd encourage you to poke through the source code of ``MinHash.py`` and take a look at the scripts as well.
Protein databases (and for that matter, arbitrary K-length strings) coming soon...
نیازمندی
| مقدار | نام |
|---|---|
| >=2.1.1 | khmer |
| - | screed |
| - | h5py |
| - | numpy |
| - | blist |
| - | argparse |
| - | pandas |
| >=24.2.0 | setuptools |
| - | six |
| - | scipy |
| - | matplotlib |
نحوه نصب
نصب پکیج whl CMash-0.3.0:
pip install CMash-0.3.0.whl
نصب پکیج tar.gz CMash-0.3.0:
pip install CMash-0.3.0.tar.gz