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Chromosome Mapping

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Machine Learning to Identify Gene Interactions from High-Throughput Mutant Crosses.

Methods in molecular biology (Clifton, N.J.)
Advances in molecular genetics through high-throughput gene mutagenesis and genetic crossing have enabled gene interaction mapping across whole genomes. Detecting gene interactions in even small microbial genomes relies on measuring growth phenotypes...

Genetic dissection of complex traits using hierarchical biological knowledge.

PLoS computational biology
Despite the growing constellation of genetic loci linked to common traits, these loci have yet to account for most heritable variation, and most act through poorly understood mechanisms. Recent machine learning (ML) systems have used hierarchical bio...

NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks.

Genome biology
Long-read sequencing enables variant detection in genomic regions that are considered difficult-to-map by short-read sequencing. To fully exploit the benefits of longer reads, here we present a deep learning method NanoCaller, which detects SNPs usin...

Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping.

Plant physiology
Stomata are adjustable pores on leaf surfaces that regulate the tradeoff of CO2 uptake with water vapor loss, thus having critical roles in controlling photosynthetic carbon gain and plant water use. The lack of easy, rapid methods for phenotyping ep...

Machine Learning Methods for Exploring Sequence Determinants of 3D Genome Organization.

Journal of molecular biology
In higher eukaryotic cells, chromosomes are folded inside the nucleus. Recent advances in whole-genome mapping technologies have revealed the multiscale features of 3D genome organization that are intertwined with fundamental genome functions. Howeve...

DeepBSA: A deep-learning algorithm improves bulked segregant analysis for dissecting complex traits.

Molecular plant
Bulked segregant analysis (BSA) is a rapid, cost-effective method for mapping mutations and quantitative trait loci (QTLs) in animals and plants based on high-throughput sequencing. However, the algorithms currently used for BSA have not been systema...

Ten quick tips for sequence-based prediction of protein properties using machine learning.

PLoS computational biology
The ubiquitous availability of genome sequencing data explains the popularity of machine learning-based methods for the prediction of protein properties from their amino acid sequences. Over the years, while revising our own work, reading submitted m...

Combining Digital and Molecular Approaches Using Health and Alternate Data Sources in a Next-Generation Surveillance System for Anticipating Outbreaks of Pandemic Potential.

JMIR public health and surveillance
Globally, millions of lives are impacted every year by infectious diseases outbreaks. Comprehensive and innovative surveillance strategies aiming at early alert and timely containment of emerging and reemerging pathogens are a pressing priority. Shor...

GEMA: A Genome Exact Mapping Accelerator Based on Learned Indexes.

IEEE transactions on biomedical circuits and systems
In this article, we introduce GEMA, a genome exact mapping accelerator based on learned indexes, specifically designed for FPGA implementation. GEMA utilizes a machine learning (ML) algorithm to precisely locate the exact position of read sequences w...

A machine learning enhanced EMS mutagenesis probability map for efficient identification of causal mutations in Caenorhabditis elegans.

PLoS genetics
Chemical mutagenesis-driven forward genetic screens are pivotal in unveiling gene functions, yet identifying causal mutations behind phenotypes remains laborious, hindering their high-throughput application. Here, we reveal a non-uniform mutation rat...