semvec/pypi-packages huggingface dataset:
transfacpred 1.1.1
A method to predict the transcription factors using protein sequences.
Requires Python: >=3.6

TransFacPred

A highly accurate method to predict the transcription factors using protein sequences.


## Introduction

TransFacPred is developed for predicting the transcription factors (TFs) using the protein primary sequence information. In this approach, Hybrid model was implemented in which is a combination of ET-based model and BLAST Search.


## Webserver and Standalone

Available as web-server at https://webs.iiitd.edu.in/raghava/transfacpred

Available as standalone at https://github.com/raghavagps/transfacpred/

## Installation


To install the Transfacpred package, use the following command:

```bash
pip install transfacpred
```


## Minimum USAGE

To explore the available options for the command-line tool, use:

```bash
transfacpred -h
```

## Full Usage

```bash
usage: transfacpred [-h] 
                       [-i INPUT 
                       [-o OUTPUT]
                       [-t THRESHOLD]
                       [-d {1,2}]


Please provide following arguments for successful run

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        Input: File name containing protein or peptide sequence in FASTA format.
  -o OUTPUT, --output OUTPUT
                        Output: File for saving results by default outfile.csv
  -t THRESHOLD, --threshold THRESHOLD
                        Threshold: Value between -0.5 to 1.5 by default -0.38
  -d {1,2}, --display {1,2}
                        Display: 1:Transcription Factors, 2: All Sequences, by default 1
```


**Input File:** It allow users to provide input in the FASTA format.

**Output File:** Program will save the results in the CSV format, in case user do not provide output file name, it will be stored in "outfile.csv".

**Threshold:** User should provide threshold between -0.5 and 1.5.


**Example usage details:**

```bash
transfacpred.py -i protein.fa
```

This will predict if the submitted sequences are TFs or Non-TFs and display only the TFs. It will use other parameters by default. It will save the output in "outfile.csv"


```bash
transfacpred -i protein.fa -o output.csv -t 0.55 -d 2
```

This will predict if the submitted sequences are TFs or Non-TFs and display all. It will save the output in "output.csv" in CSV (comma seperated variables).


# Reference
Patiyal et al. (2022) A hybrid approach for predicting transcription factors. <a href="https://doi.org/10.1101/2022.07.13.499865">Bioxriv doi: https://doi.org/10.1101/2022.07.13.499865 </a>
nozomi 0.0.269
HTTP web application & API protocol library
Requires Python: >=3.6
Keywords: library http api web application

# Nozomi

Nozomi is a collection of Python protocols that, when implemented, give life to a web application or HTTP Application Programming Interface (API).

## State of play

Nozomi is a distillation of concepts developed in the production of the Amatino API and web application. It is a work in progress, and should not yet be used by anyone for anything.

To be notified when Nozomi is ready for production use, signup to the Amatino newsletter: https://amatino.io/newsletter
feco3 0.5.0
A Rust-backed Python library for parsing .fec files.
Requires Python: >=3.7
Keywords: fec,fec-file,rust,pyo3,arrow,pyarrow

# FECo3

Python bindings for a `.fec` file parser in rust.

Install with `pip install feco3`

- [API docs](https://nickcrews.github.io/feco3/)
- [Repository](https://github.com/NickCrews/feco3)

## Example

```python
import pyarrow as pa
import feco3

# ruff: noqa: E501

# You can supply a URL or a path to a file.
src = "https://docquery.fec.gov/dcdev/posted/1002596.fec"
# src = "path/to/file.fec"
# src = pathlib.Path("path/to/file.fec")

# This doesn't actually read or parse any data yet
fec = feco3.FecFile(src)
print(fec)
# FecFile(src='https://docquery.fec.gov/dcdev/posted/1002596.fec')

# Only when we access something do we actually start parsing.
# Still, we only parse as far as we need to, so this is quite fast.
# This is useful, for example, if you only need the header or cover,
# or if you only want to look at the itemizations in certain forms.
print(fec.header)
print(fec.cover)
# Header(fec_version='8.1', software_name='NetFile', software_version='199199', report_id=None, report_number='0')
# Cover(form_type='F3N', filer_committee_id='C00479188')

# Iterate through the itemizations in the file in batches of pyarrow RecordBatches.
# By iterating, this keeps us from having to load the entire file into memory.
# By using pyarrow, we can avoid copying the underlying data from Rust to Python.
# It integrates well with the rest of the Python data ecosystem, for example
# it's easy to convert to a pandas DataFrames.
batcher = feco3.PyarrowBatcher(fec, max_batch_size=1024 * 1024)
for batch in batcher:
    # The record code for this kind of itemizations, eg. 'SA11AI'
    assert isinstance(batch.code, str)
    # A pyarrow RecordBatch of the itemizations
    assert isinstance(batch.records, pa.RecordBatch)
    df = batch.records.to_pandas()
    print(batch.code)
    print(df.head(3))
# SA15
#   filer_committee_id_number transaction_id back_reference_tran_id_number back_reference_sched_name  ... conduit_zip_code memo_code memo_text_description reference_code
# 0                 C00479188        INCA994                                                          ...
# 1                 C00479188        INCA992                                                          ...
# 2                 C00479188        INCA993                                                          ...

# [3 rows x 44 columns]
# TEXT
#   filer_committee_id_number transaction_id_number back_reference_tran_id_number back_reference_sched_form_name            text
# 0                 C00479188              TPAYC760                       PAYC760                          SC/10  PERSONAL FUNDS
# SC/10
#   filer_committee_id_number transaction_id_number receipt_line_number entity_type  ... lender_candidate_state lender_candidate_district memo_code memo_text_description
# 0                 C00479188               PAYC760                 13B         CAN  ...

# [1 rows x 37 columns]                       ...

```