COMIT: identification of noncoding motifs under selection in coding sequences

COMIT is presented; an algorithm for detecting functional non-coding motifs in coding regions, separating nucleotide and amino acid effects

Current tools for the identification of miRNA genes and their targets

The discovery of microRNAs (miRNAs), almost 10 years ago, changed dramatically our perspective on eukaryotic gene expression regulation. However, the broad and important functions of these regulators are only now becoming apparent. The expansion of our catalogue of miRNA genes and the identification of the genes they regulate owe much to the development of sophisticated computational tools that have helped either to focus or interpret experimental assays. In this article, we review the methods for miRNA gene finding and target identification that have been proposed in the last few years. We identify some problems that current approaches have not yet been able to overcome and we offer some perspectives on the next generation of computational methods

A high-resolution map of human evolutionary constraint using 29 mammals.

The comparison of related genomes has emerged as a powerful lens for genome interpretation. Here we report the sequencing and comparative analysis of 29 eutherian genomes. We confirm that at least 5.5% of the human genome has undergone purifying selection, and locate constrained elements covering ∼4.2% of the genome. We use evolutionary signatures and comparisons with experimental data sets to suggest candidate functions for ∼60% of constrained bases. These elements reveal a small number of new coding exons, candidate stop codon readthrough events and over 10,000 regions of overlapping synonymous constraint within protein-coding exons. We find 220 candidate RNA structural families, and nearly a million elements overlapping potential promoter, enhancer and insulator regions. We report specific amino acid residues that have undergone positive selection, 280,000 non-coding elements exapted from mobile elements and more than 1,000 primate- and human-accelerated elements. Overlap with disease-associated variants indicates that our findings will be relevant for studies of human biology, health and disease

The specificity and evolution of gene regulatory elements

Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references.The regulation of gene expression underlies the morphological, physiological, and functional differences between human cell types, developmental stages, and healthy and disease states. Gene regulation in eukaryotes is controlled by a complex milieu including transcription factors, microRNAs (miRNAs), cis-regulatory DNA and RNA. It is the quantitative and combinatorial interactions of these regulatory elements that defines gene expression, but these interactions are incompletely understood. In this thesis, I present two new methods for determining the quantitative specificity of gene regulatory factors. First, I present a comparative genomics approach that utilizes signatures of natural selection to detect the conserved biological relevance of miRNAs and their targets. Using this method, I quantify the abundance of different conserved miRNA target types, including different seed matches and 30-compensatory targets. I show that over 60% of mammalian mRNAs are conserved targets of miRNAs and that a surprising amount of conserved miRNA targeting is mediated by seed matches with relatively low efficacy. Extending this method from mammals to other organisms, I find that miRNA targeting rules are mostly conserved, although I show evidence for new types of miRNA targets in nematodes. Taking advantage of variations in 30 UTR lengths between species, I describe general properties of miRNA targeting that are affected by 30 UTR length. Finally, I introduce a new, high-throughput assay for the quantification of transcription factor in vitro binding affinity to millions of sequences. I apply this method to GCN4, a yeast transcription factor, and reconstruct all known properties of its binding preferences. Additionally, I discover some new subtleties in its specificity and estimate dissociation constants for hundreds of thousands of sequences. I verify the utility of the binding affinities by comparing to in vivo binding data and to the regulatory response following GCN4 induction.by Robin Carl Friedman.Ph.D

GraphProt: modeling binding preferences of RNA-binding proteins

We present GraphProt, a computational framework for learning sequence- and structure-binding preferences of RNA-binding proteins (RBPs) from high-throughput experimental data. We benchmark GraphProt, demonstrating that the modeled binding preferences conform to the literature, and showcase the biological relevance and two applications of GraphProt models. First, estimated binding affinities correlate with experimental measurements. Second, predicted Ago2 targets display higher levels of expression upon Ago2 knockdown, whereas control targets do not. Computational binding models, such as those provided by GraphProt, are essential for predicting RBP binding sites and affinities in all tissues. GraphProt is freely available at http://www.bioinf.uni-freiburg.de/Software/GraphProt

Using a kernel density estimation based classifier to predict species-specific microRNA precursors

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are short non-coding RNA molecules participating in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs) over the years. Recently, <it>ab initio </it>approaches obtain more attention because that they can discover species-specific pre-miRNAs. Most <it>ab initio </it>approaches proposed novel features to characterize RNA molecules. However, there were fewer discussions on the associated classification mechanism in a miRNA predictor.</p> <p>Results</p> <p>This study focuses on the classification algorithm for miRNA prediction. We develop a novel <it>ab initio </it>method, miR-KDE, in which most of the features are collected from previous works. The classification mechanism in miR-KDE is the relaxed variable kernel density estimator (RVKDE) that we have recently proposed. When compared to the famous support vector machine (SVM), RVKDE exploits more local information of the training dataset. MiR-KDE is evaluated using a training set consisted of only human pre-miRNAs to predict a benchmark collected from 40 species. The experimental results show that miR-KDE delivers favorable performance in predicting human pre-miRNAs and has advantages for pre-miRNAs from the genera taxonomically distant to humans.</p> <p>Conclusion</p> <p>We use a novel classifier of which the characteristic of exploiting local information is particularly suitable to predict species-specific pre-miRNAs. This study also provides a comprehensive analysis from the view of classification mechanism. The good performance of miR-KDE encourages more efforts on the classification methodology as well as the feature extraction in miRNA prediction.</p

Deciphering the Hidden Language of Long Non-Coding RNAs: Recent Findings and Challenges

Long non-coding RNAs (lncRNAs) are crucial non-coding RNA genes involved in diverse cellular processes. However, the mechanisms underlying their emergence and functions remain incompletely understood. A major challenge in the field is to understand how lncRNA sequences affect their function. In recent years, comprehensive genetic and genomic studies have started to unfold the function of lncRNAs through their interactions, cellular organization, and structure. This comprehensive review delves into the intricate interplay between lncRNA sequences and their functional implications. Unlike other RNA types, lncRNAs exhibit a complex syntax, employing diverse functional elements such as protein recognition and miRNA binding sites, repeat elements, secondary structures, and non-canonical interactions with RNA and DNA binding proteins. By unraveling the hidden language that governs the function and classification of lncRNAs, we aim to shed light on the underlying principles shaping their diverse functions. Through a detailed examination of the intricate relationship between lncRNA sequences and their biological effects, this review offers insights into the sequences underlying lncRNA functionality. Understanding the unique sequence characteristics and functional elements employed by lncRNAs has the potential to advance our knowledge of gene regulation and cellular processes, providing a foundation for the development of novel therapeutic strategies and targeted interventions

생물학적 서열 데이터에 대한 표현 학습

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2021.8. 윤성로.As we are living in the era of big data, the biomedical domain is not an exception. With the advent of technologies such as next-generation sequencing, developing methods to capitalize on the explosion of biomedical data is one of the most major challenges in bioinformatics. Representation learning, in particular deep learning, has made significant advancements in diverse fields where the artificial intelligence community has struggled for many years. However, although representation learning has also shown great promises in bioinformatics, it is not a silver bullet. Off-the-shelf applications of representation learning cannot always provide successful results for biological sequence data. There remain full of challenges and opportunities to be explored. This dissertation presents a set of representation learning methods to address three issues in biological sequence data analysis. First, we propose a two-stage training strategy to address throughput and information trade-offs within wet-lab CRISPR-Cpf1 activity experiments. Second, we propose an encoding scheme to model interaction between two sequences for functional microRNA target prediction. Third, we propose a self-supervised pre-training method to bridge the exponentially growing gap between the numbers of unlabeled and labeled protein sequences. In summary, this dissertation proposes a set of representation learning methods that can derive invaluable information from the biological sequence data.우리는 빅데이터의 시대를 맞이하고 있으며, 의생명 분야 또한 예외가 아니다. 차세대 염기서열 분석과 같은 기술들이 도래함에 따라, 폭발적인 의생명 데이터의 증가를 활용하기 위한 방법론의 개발은 생물정보학 분야의 주요 과제 중의 하나이다. 심층 학습을 포함한 표현 학습 기법들은 인공지능 학계가 오랫동안 어려움을 겪어온 다양한 분야에서 상당한 발전을 이루었다. 표현 학습은 생물정보학 분야에서도 많은 가능성을 보여주었다. 하지만 단순한 적용으로는 생물학적 서열 데이터 분석의 성공적인 결과를 항상 얻을 수는 않으며, 여전히 연구가 필요한 많은 문제들이 남아있다. 본 학위논문은 생물학적 서열 데이터 분석과 관련된 세 가지 사안을 해결하기 위해, 표현 학습에 기반한 일련의 방법론들을 제안한다. 첫 번째로, 유전자가위 실험 데이터에 내재된 정보와 수율의 균형에 대처할 수 있는 2단계 학습 기법을 제안한다. 두 번째로, 두 염기 서열 간의 상호 작용을 학습하기 위한 부호화 방식을 제안한다. 세 번째로, 기하급수적으로 증가하는 특징되지 않은 단백질 서열을 활용하기 위한 자기 지도 사전 학습 기법을 제안한다. 요약하자면, 본 학위논문은 생물학적 서열 데이터를 분석하여 중요한 정보를 도출할 수 있는 표현 학습에 기반한 일련의 방법론들을 제안한다.1 Introduction 1 1.1 Motivation 1 1.2 Contents of Dissertation 4 2 Background 8 2.1 Representation Learning 8 2.2 Deep Neural Networks 12 2.2.1 Multi-layer Perceptrons 12 2.2.2 Convolutional Neural Networks 14 2.2.3 Recurrent Neural Networks 16 2.2.4 Transformers 19 2.3 Training of Deep Neural Networks 23 2.4 Representation Learning in Bioinformatics 26 2.5 Biological Sequence Data Analyses 29 2.6 Evaluation Metrics 32 3 CRISPR-Cpf1 Activity Prediction 36 3.1 Methods 39 3.1.1 Model Architecture 39 3.1.2 Training of Seq-deepCpf1 and DeepCpf1 41 3.2 Experiment Results 44 3.2.1 Datasets 44 3.2.2 Baselines 47 3.2.3 Evaluation of Seq-deepCpf1 49 3.2.4 Evaluation of DeepCpf1 51 3.3 Summary 55 4 Functional microRNA Target Prediction 56 4.1 Methods 62 4.1.1 Candidate Target Site Selection 63 4.1.2 Input Encoding 64 4.1.3 Residual Network 67 4.1.4 Post-processing 68 4.2 Experiment Results 70 4.2.1 Datasets 70 4.2.2 Classification of Functional and Non-functional Targets 71 4.2.3 Distinguishing High-functional Targets 73 4.2.4 Ablation Studies 76 4.3 Summary 77 5 Self-supervised Learning of Protein Representations 78 5.1 Methods 83 5.1.1 Pre-training Procedure 83 5.1.2 Fine-tuning Procedure 86 5.1.3 Model Architecturen 87 5.2 Experiment Results 90 5.2.1 Experiment Setup 90 5.2.2 Pre-training Results 92 5.2.3 Fine-tuning Results 93 5.2.4 Comparison with Larger Protein Language Models 97 5.2.5 Ablation Studies 100 5.2.6 Qualitative Interpreatation Analyses 103 5.3 Summary 106 6 Discussion 107 6.1 Challenges and Opportunities 107 7 Conclusion 111 Bibliography 113 Abstract in Korean 130박

Cell-type specific analysis of translating RNAs in developing flowers reveals new levels of control

Determining both the expression levels of mRNA and the regulation of its translation is important in understanding specialized cell functions. In this study, we describe both the expression profiles of cells within spatiotemporal domains of the Arabidopsis thaliana flower and the post-transcriptional regulation of these mRNAs, at nucleotide resolution. We express a tagged ribosomal protein under the promoters of three master regulators of flower development. By precipitating tagged polysomes, we isolated cell type specific mRNAs that are probably translating, and quantified those mRNAs through deep sequencing. Cell type comparisons identified known cell-specific transcripts and uncovered many new ones, from which we inferred cell type-specific hormone responses, promoter motifs and coexpressed cognate binding factor candidates, and splicing isoforms. By comparing translating mRNAs with steady-state overall transcripts, we found evidence for widespread post-transcriptional regulation at both the intron splicing and translational stages. Sequence analyses identified structural features associated with each step. Finally, we identified a new class of noncoding RNAs associated with polysomes. Findings from our profiling lead to new hypotheses in the understanding of flower development

Evolutionary Approaches to the Study of Small Noncoding Regulatory RNA Pathways: A Dissertation

Short noncoding RNAs play roles in regulating nearly every biological process, in nearly every organism, yet the exact function and importance of these molecules remains a subject of some debate. In order to gain a better understanding of the contexts in which these regulators have evolved, I have undertaken a variety of approaches to study the evolutionary history of the components that make up these pathways, in the form of two main research efforts. In the first chapter, I have used a combination of population genetics and molecular evolution techniques to show that proteins involved in the piRNA pathway are rapidly evolving, and that different components of the pathway seem to be evolving rapidly on different timescales. These rapidly evolving piRNA pathway proteins can be loosely separated into two groups. The first group appears to evolve quickly at the species level, perhaps in response to transposons that invade across species lines, while the second group appears to evolve quickly at the level of individual populations, perhaps in response to transposons that are paternally present yet novel to the maternal genome. In the second chapter of my research, I have used molecular evolution techniques and carefully devised controls to show that the binding sites of well-conserved miRNAs are among the most slowly changing short motifs in the genome, consistent with a conserved function for these short RNAs in regulatory pathways that are ancient and extremely slow to change. I have additionally discovered a major flaw in an existing approach to motif turnover calculations, which may lead to systematic biases in the published literature toward the false inference of increased regulatory complexity over time. I have implemented a revised approach to motif turnover that addresses this flaw