A Modular Open Source Framework for Genomic Variant Calling
Variant calling is a fundamental task in genomic research, essential for detecting genetic variations such as single nucleotide polymorphisms (SNPs) and insertions or deletions (indels). This paper presents an enhancement to DeepChem, a widely used open-source drug discovery framework, through the integration of DeepVariant. In particular, we introduce a variant calling pipeline that leverages DeepVariant's convolutional neural network (CNN) architecture to improve the accuracy and reliability of variant detection. The implemented pipeline includes stages for realignment of sequencing reads, candidate variant detection, and pileup image generation, followed by variant classification using a modified Inception v3 model. Our work adds a modular and extensible variant calling framework to the DeepChem framework and enables future work integrating DeepChem's drug discovery infrastructure more tightly with bioinformatics pipelines.
Code (0)
등록된 구현이 없습니다.
Tasks
Drug DiscoveryImage GenerationSimilar Papers 제목 키워드 기반
Beyond GeneGPT: A Multi-Agent Architecture with Open-Source LLMs for Enhanced Genomic Question Answering
Genomic question answering often requires complex reasoning and integration across diverse biomedical sources. GeneGPT addressed this challenge by combining domain-specific APIs with OpenAI's code-davinci-002 large langu…
Question AnsweringThe Canadian VirusSeq Data Portal & Duotang: open resources for SARS-CoV-2 viral sequences and genomic epidemiology
The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform public health response. Millions of SARS-CoV-2 genome sequences have been deposit…
EpidemiologyAutonomous Agent-Orchestrated Digital Twins (AADT): Leveraging the OpenClaw Framework for State Synchronization in Rare Genetic Disorders
Background: Medical Digital Twins (MDTs) are computational representations of individual patients that integrate clinical, genomic, and physiological data to support diagnosis, treatment planning, and outcome prediction.…
OmniGenBench: A Modular Platform for Reproducible Genomic Foundation Models Benchmarking
The code of nature, embedded in DNA and RNA genomes since the origin of life, holds immense potential to impact both humans and ecosystems through genome modeling. Genomic Foundation Models (GFMs) have emerged as a trans…
BenchmarkingIncorporating LLM Embeddings for Variation Across the Human Genome
Recent advances in large language model (LLM) embeddings have enabled powerful representations for biological data, but most applications to date focus on gene-level information. We present one of the first systematic fr…