azkasaleem Goto Github PK

followers: 9.0 following: 16.0 repos: 43.0 gists: 0.0

Name: Azka Saleem

Type: User

Company: NWAFU, China.

Bio: Bioinformatician | Enthusiastic into learning Data science

Twitter: azka_saleem11

Location: China

Blog: https://www.linkedin.com/in/azka-saleem-57817b132/

It's Azka Saleem

Intrested in Bioinformatics, DataScience & ML

I had my Masters in Biotechnology and developed my interest in Data science, Machine Learning and Deep Learning. I'm Learning new things daily and Aims to put bigger things through merging them all.

🌍 I'm based in Pakistan currently.
✉️ You can contact me at [email protected]
🧠 I'm learning Omics, Bioinformatics, Data science, Machine learning, Deep learning
⚡ You can follow me on my Socials to stay connected and enjoy my updates!

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Top Repositories

Azka Saleem's Projects

ads1-notebooks

Copies of notebooks used in the practical sessions for Algorithms for DNA Sequencing

azkasaleem_machinelearning_practice

bioinformatics-specialization-coursera

Bioinformatics Specialization Journey to the Frontier of Computational Biology. Master bioinformatics software and computational approaches in modern biology.

bioinformatics-workflows

minimal example implementations for bioinformatics workflow managers

computational-genomics-with-r-2020-

Book

deepm6aseq

a deep-learning-based framework for studying m6A

dimer

Dimer is a Python-based package for testing differential methylation in m6A sequencing data.

eda_pandas_profiling-app

This app is a Streamlit dashboard that allows you to explore the data in the dataset.

epinano

Detection of RNA modifications from Oxford Nanopore direct RNA sequencing reads (Liu*, Begik* et al., Nature Comm 2019)

exomepeak

Analysis of RNA epitranscriptome sequencing data with affinity-based shotgun sequencing approach

funm6aviewer

Identification and visualization of functional differential m6A methylation genes (FDmMGenes) and single base DmM sites.

We present HRIBO (High-throughput annotation by Ribo-seq), a workflow to enable reproducible and high-throughput analysis of bacterial Ribo-seq data. The workflow performs all required pre-processing steps and quality control. Importantly, HRIBO outputs annotation-independent ORF predictions based on two complementary prokaryotic-focused tools, an