Immunogenetics in the rabbit.

This chapter on immunogenetics in the rabbit focused on some genes with genetic and genomic sequence information including those encoding: soluble circulating immunoglobulin molecules (Igs) and their surface-bound forms on B lymphocytes (BCRs); T-cell receptors on T lymphocyte surfaces, (TCRs); the rabbit Leukocyte Antigen (RLA) complex (proteins on cells that function to present antigen fragments to TCRs); and some cytokine genes that encode key regulators of T- and B-cell responses.

Metabolic pathway inference using multi-label classification with rich pathway features

Metabolic inference from genomic sequence information is a necessary step in determining the capacity of cells to make a living in the world at different levels of biological organization. A common method for determining the metabolic potential encoded in genomes is to map conceptually translated open reading frames onto a database containing known product descriptions. Such gene-centric methods are limited in their capacity to predict pathway presence or absence and do not support standardized rule-sets for automated and reproducible research. Pathway-centric methods based on defined rule sets or machine learning algorithms provide an adjunct or alternative inference method that supports hypothesis generation and testing of metabaolic relationships within and between cells. Here, we present mlLGPR, multi-label based on logistic regression for pathway prediction, a software package that uses supervised multi-label classification and rich pathway features to infer metabolic networks at the individual, population and community levels of organization. We evaluated mlLGPR performance using a corpora of 12 experimental datasets manifesting diverse multi-label properties, including manually curated organismal genomes, synthetic microbial communities and low complexity microbial communities. Resulting performance metrics equaled or exceeded previous reports for organismal genomes and identify specific challenges associated with features engineering and training data for community-level metabolic inference.Author summaryPredicting the complex series of metabolic interactions e.g. pathways, within and between cells from genomic sequence information is an integral problem in biology linking genotype to phenotype. This is a prerequisite to both understanding fundamental life processes and ultimately engineering these processes for specific biotechnological applications. A pathway prediction problem exists because we have limited knowledge of the reactions and pathways operating in cells even in model organisms like Esherichia coli where the majority of protein functions are determined. To improve pathway prediction outcomes for genomes at different levels of complexity and completion we have developed mlLGPR, multi-label based on logistic regression for pathway prediction, a scalable open source software package that uses supervised multi-label classification and rich pathway features to infer metabolic networks. We benchmark mlLGPR performance against other inference methods providing a code base and metrics for continued application of machine learning methods to the pathway prediction problem at the individual, population and community levels of biological organization.

A regulatory-sequence classifier with a neural network for genomic information processing

Genotype-phenotype mapping is one of the fundamental challenges in biology. The difficulties stem in part from the large amount of sequence information and the puzzling genomic code, particularly of non-protein-coding regions such as gene regulatory sequences. However, recently deep learning–based methods were shown to have the ability to decipher the gene regulatory code of genomes. Still, prediction accuracy needs improvement. Here, we report the design of convolution layers that efficiently process genomic sequence information and developed a software, DeepGMAP, to train and compare different deep learning-based models (https://github.com/koonimaru/DeepGMAP). First, we demonstrate that our convolution layers, termed forward- and reverse-sequence scan (FRSS) layers, enhance the power to predict gene regulatory sequences. Second, we assessed previous studies and identified problems associated with data structures that caused overfitting. Finally, we introduce several visualization methods that provide insights into the syntax of gene regulatory sequences.

The selenium content of SEPP1 versus selenium requirements in vertebrates

Selenoprotein P (SEPP1) distributes selenium (Se) throughout the body via the circulatory system. The Se content of SEPP1 varies from 7 to 18 Se atoms depending on the species, but the reason for this variation remains unclear. Herein we provide evidence that vertebrate SEPP1 Sec content correlates positively with Se requirements (R2=0.88). As the Se content of full length SEPP1 is genetically determined, this presents a unique case where a nutrient requirement can be predicted based on genomic sequence information.

The selenium content of SEPP1 versus selenium requirements in vertebrates

Selenoprotein P (SEPP1) distributes selenium (Se) throughout the body via the circulatory system. The Se content of SEPP1 varies from 7 to 18 Se atoms depending on the species, but the reason for this variation remains unclear. Herein we provide evidence that vertebrate SEPP1 Sec content correlates positively with Se requirements (R2=0.88). As the Se content of full length SEPP1 is genetically determined, this presents a unique case where a nutrient requirement can be predicted based on genomic sequence information.