Using Structural and Evolutionary Information to Detect and Correct Pyrosequencing Errors in Noncoding RNAs.

Extended version of RECOMB'13International audienceThe analysis of the sequence-structure relationship in RNA molecules is not only essential for evolutionary studies but also for concrete applications such as error-correction in next generation sequencing (NGS) technologies. The prohibitive sizes of the mutational and conformational landscapes, combined with the volume of data to process, require efficient algorithms to compute sequence-structure properties. In this article, we address the correction of NGS errors by calculating which mutations most increase the likelihood of a sequence to a given structure and RNA family. We introduce RNApyro, an efficient, linear time and space inside-outside algorithm that computes exact mutational probabilities under secondary structure and evolutionary constraints given as a multiple sequence alignment with a consensus structure. We develop a scoring scheme combining classical stacking base-pair energies to novel isostericity scores and apply our techniques to correct pointwise errors in 5s and 16s rRNA sequences. Our results suggest that RNApyro is a promising algorithm to complement existing tools in the NGS error-correction pipeline

Combining structure probing data on RNA mutants with evolutionary information reveals RNA-binding interfaces

International audienceSystematic structure probing experiments (e.g. SHAPE) of RNA mutants such as the mutate-and-map protocol give us a direct access into the genetic robustness of ncRNA structures. Comparative studies of homologous sequences provide a distinct, yet complementary, approach to analyze structural and functional properties of non-coding RNAs. In this paper, we introduce a formal framework to combine the biochemical signal collected from mutate-and-map experiments, with the evolutionary information available in multiple sequence alignments. We apply neutral theory principles to detect complex long-range dependencies between nucleotides of a single stranded RNA, and implement these ideas into a software called aRNhAck. We illustrate the biological significance of this signal and show that the nucleotides networks calculated with aRNhAck are correlated with nucleotides located in RNA-RNA, RNA-protein, RNA-DNA and RNA-ligand interfaces. aRNhAck is freely available at http://csb.cs.mcgill.ca/arnhack

Next-Generation Sequencing — An Overview of the History, Tools, and “Omic” Applications

Next-generation sequencing (NGS) technologies using DNA, RNA, or methylation sequencing have impacted enormously on the life sciences. NGS is the choice for large-scale genomic and transcriptomic sequencing because of the high-throughput production and outputs of sequencing data in the gigabase range per instrument run and the lower cost compared to the traditional Sanger first-generation sequencing method. The vast amounts of data generated by NGS have broadened our understanding of structural and functional genomics through the concepts of “omics” ranging from basic genomics to integrated systeomics, providing new insight into the workings and meaning of genetic conservation and diversity of living things. NGS today is more than ever about how different organisms use genetic information and molecular biology to survive and reproduce with and without mutations, disease, and diversity within their population networks and changing environments. In this chapter, the advances, applications, and challenges of NGS are reviewed starting with a history of first-generation sequencing followed by the major NGS platforms, the bioinformatics issues confronting NGS data storage and analysis, and the impacts made in the fields of genetics, biology, agriculture, and medicine in the brave, new world of ”omics.

Multi-Platform Next-Generation Sequencing of the Domestic Turkey (Meleagris gallopavo): Genome Assembly and Analysis

The combined application of next-generation sequencing platforms has provided an economical approach to unlocking the potential of the turkey genome

Application of statistical analysis in transcriptomic and metagenomic data

학위논문(석사)--서울대학교 대학원 :자연과학대학 협동과정 생물정보학전공,2019. 8. 김희발.With the advance in sequencing technology, genomics, transcriptomics, proteomics, epigenomics and metagenomics study genetic materials on the genome-wide scale. Transcriptome and metagenomics have common tools and methods for analysis because they use quantitative data. Analysis of transcriptome data aims at quantifying gene expression and finding differentially expressed genes (DEG) under the certain condition. To detect DEG and trait-associated genes, various statistical methods and tools have been developed and some of them are widely being used. As analysis of metagenome data also use quantified abundance of microorganisms, some developed tools for transcriptome analysis were also applied in metagenome data. In this thesis, I described how the statistical methods were employed to solve biological problems in quantitative data. Analysis of quantitative data recently employed machine learning based methods to predict traits including disease and healthy status. Especially, as gut microbiome is associated with hosts health, several studies suggested the possibility to diagnosis the diseases using abundance and kinds of microorganisms living in gut. In Chapter 2, machine learning based multi-classifier algorithms were established and evaluated to classify several diseases using gut microbiome data. LogitBoost algorithms and using abundance of microorganisms at genus level showed the highest performance. By selecting microorganisms to enhance the performance, the selected microorganisms were suggested as markers to classify various disease simultaneously. Gut microbiome is used as significant marker not only in human health but also in domestic animals. For example, microbial communities altered by feeds in broiler chickens. In Chapter 3, the effect of A. hookeri on gut microbiome in young broiler chickens was investigated. Statistical test revealed that the composition of microbiome was altered by supplement with A. hookeri leaf. The modulated gut microbiome by leaf was correlated with growth traits including body weight, bone strength, and infectious bursal disease antibody. For more accurate analysis of quantitative data, accurate quantification of genetic materials is essential. Chapter 3 suggests the cause of mis-quantification of mRNAs and solutions to reduce the mis-quantified expression. Long non-coding RNAs, which are overlapped with mRNAs in genomic position, can be mis-quantified to the overlapped mRNAs. Simulation showed the degree of errors by such mis-quantification. Tools for alignment and quantification were compared to reduce the error and achieve more accurate quantification for transcriptome.시퀀싱 기술의 발달로 유전체, 전사체, 단백체, 후성 유전체, 메타지노믹와 같은 분야에서 유전체 단위로 생명체의 정보를 해독할 수 있게 되었다. 이 중 전사체와 메타지놈 분야는 정량화된 유전 정보를 다룬다는 공통점 때문에 통계적 분석 방법들을 공유하고 있다. 전사체 분석은 유전자들의 발현을 정량화 하고, 특정 조건 하에 다른 양으로 발현되는 유전자를 발굴하는 것을 목표하고 있다. 정량화된 양을 그룹 간 비교하는 거나 형질과 관련된 유전자를 찾기 위해 여러 통계적 분석 도구 및 방법들이 개발되었으며 널리 사용되고 있다. 메타지놈 분석은 정량화 하는 대상이 미생물들의 양이라는 것은 다르나 정량화한 미생물들의 양을 분석하기 때문에 전사체에서 사용되었던 방법들이 대부분 이용되고 있다. 양적 자료의 기본적인 분석에서 더 나아가서 머신러닝기법을 이용하여 정량화된 유전물질의 양으로 질병과 같은 형질을 예측하고자 하는 시도도 이루어 지고 있다. 특히, 인간의 장내미생물은 면역체계와 연관성이 있기 때문에 장내미생물의 종류와 양으로 질병을 진단하려는 여러 연구가 보고 되었다. 제 2장에서는 다양한 질병을 가진 환자들의 장내미생물을 이용하여 머신러닝기반 다중 분류 알고리즘으로 질병을 분류할 수 있는 모델을 구축하고 이를 평가하였다. 이 연구를 통해 LogitBoost 기반 예측 모델이 6 가지 질병을 가장 잘 구분 짓다는 것을 밝혔고, 미생물의 분류체계 중 속(genus)에서의 양을 이용했을 때 성능이 가장 좋다는 것을 보였다. 또한 미생물들을 선택하여 모델의 성능을 높이는 과정에서 다양한 질병을 동시에 구분하는 미생물들을 질병 진단을 위한 마커로 제시하였다. 인간에서 뿐만아니라 동물들에서도 장내미생물의 조성은 건강 및 생산량의 중요한 지표로 이용되고 있다. 예를들어, 사료에 따라 육계의 장내미생물 조성의 변화는 과거 연구에서 보고되어 왔다. 3장에서는 삼채를 복용한 육계의 장내 미생물을 조사하고, 생산성과 연관이 있는 장내미생물들을 발굴하였다. 삼채의 잎을 복용은 육계의 장내미생물에 영향을 미치는 것을 밝혀내었으며, 연관성 분석을 통해서 삼채의 복용에 영향을 받는 미생물들이 육계의 체중, 경골강도 및 면역과 관련되어 있다 것을 제시하였다. 또한 미생물 기능분석을 통해 미생물 조성의 변화가 탄수화물 대사를 증진시킨다는 단서를 제시하였다. 정량적 데이터들의 좀 더 정확한 분석을 위해서는 정량화 단계에서 유전물질의 정확한 측정이 무엇보다 중요하다. 3장에서는 전사체 분석에서 mRNA의 발현량이 정확히 측정되지 못하게 하는 에러요인을 제시하고 이를 해결하기 위한 방법들을 제시하였다. 유전체 서열에서 mRNA와 중첩되어 있는 lncRNA는 정량화의 알고리즘상 mRNA로 오인될 수 있다는 것을 가정하였으며 시뮬레이션을 통해 lncRNA 발현량임에도 mRNA로 정량화되는 에러율을 제시하였다. 이러한 에러를 해결하기 위해서 정량화 단계에서 쓰이는 여러 알고리즘과 툴을 비교하여 더 정확한 정령화를 통한 전사체 분석을 가능하도록 하였다.Abstract 4 Contents 7 List of Tables 9 List of Figures 10 Chapter 1. Literature Review 13 1.1 Machine learning approaches for gut microbiome data 14 1.2 Community analysis in the metagenomic data 16 1.3 Quantification of mRNAs and lncRNAs 17 Chapter 2. Establishment and evaluation of prediction model for multiple disease classification based on gut microbial data 20 2.1 Abstract 21 2.2 Introduction 22 2.3 Materials and Methods 25 2.4 Results 28 2.5 Discussion 42 Chapter 3. Effects of Allium hookeri leaf on gut microbiome related to growth performance of young broiler chickens 52 3.1 Abstract 53 3.2 Introduction 54 3.3 Materials and methods 57 3.4 Result 60 3.5 Discussion 73 Chapter 4. The overlap between lncRNA and mRNA causes misleading transcripts quantification: A comprehensive evaluation of quantification for RNA-Seq data 82 4.1 Abstract 83 4.2 Introduction 85 4.3 Materials and Methods 88 4.4 Results 96 4.5 Discussion 117 References 137 요약(국문초록) 152Maste

Emerging Topics in Genome Sequencing and Analysis

This dissertation studies the emerging topics in genome sequencing and analysis with DNA and RNA. The optimal hybrid sequencing and assembly for accurate genome reconstruction and efficient detection approaches for novel ncRNAs in genomes are discussed. The next-generation sequencing is a significant topic that provides whole genetic information for the further biological research. Recent advances in high-throughput genome sequencing technologies have enabled the systematic study of various genomes by making whole genome sequencing affordable. To date, many hybrid genome assembly algorithms have been developed that can take reads from multiple read sources to reconstruct the original genome. An important aspect of hybrid sequencing and assembly is that the feasibility conditions for genome reconstruction can be satisfied by different combinations of the available read sources, opening up the possibility of optimally combining the sources to minimize the sequencing cost while ensuring accurate genome reconstruction. In this study, we derive the conditions for whole genome reconstruction from multiple read sources at a given confidence level and also introduce the optimal strategy for combining reads from different sources to minimize the overall sequencing cost. We show that the optimal read set, which simultaneously satisfies the feasibility conditions for genome reconstruction and minimizes the sequencing cost, can be effectively predicted through constrained discrete optimization. The availability of genome-wide sequences for a variety of species provides a large database for the further RNA analysis with computational methods. Recent studies have shown that noncoding RNAs (ncRNAs) are known to play crucial roles in various biological processes, and some ncRNAs are related to the genome stability and a variety of inherited diseases. The discovery of novel ncRNAs is hence an important topic, and there is a pressing need for accurate computational detection approaches that can be used to efficiently detect novel ncRNAs in genomes. One important issue is RNA structure alignment for comparative genome analysis, as RNA secondary structures are better conserved than the RNA sequences. Simultaneous RNA alignment and folding algorithms aim to accurately align RNAs by predicting the consensus structure and alignment at the same time, but the computational complexity of the optimal dynamic programming algorithm for simultaneous alignment and folding is extremely high. In this work, we proposed an innovative method, TOPAS, for RNA structural alignment that can efficiently align RNAs through topological networks. Although many ncRNAs are known to have a well conserved secondary structure, which provides useful clues for computational prediction, the prediction of ncRNAs is still challenging, since it has been shown that a structure-based approach alone may not be sufficient for detecting ncRNAs in a single sequence. In this study, we first develop a new approach by utilizing the n-gram model to classify the sequences and extract effective features to capture sequence homology. Based on this approach, we propose an advanced method, piRNAdetect, for reliable computational prediction of piRNAs in genome sequences. Utilizing the n-gram model can enhance the detection of ncRNAs that have sparse folding structures with many unpaired bases. By incorporating the n-gram model with the generalized ensemble defect, which assesses structure conservation and conformation to the consensus structure, we further propose RNAdetect, a novel computational method for accurate detection of ncRNAs through comparative genome analysis. Extensive performance evaluation based on the Rfam database and bacterial genomes demonstrates that our approaches can accurately and reliably detect novel ncRNAs, outperforming the current advanced methods

Changes in DNA methylation patterns in mammals with senescence, ageing and energy restriction

PhD ThesisDNA methylation is a reversible and inheritable chemical modification which involves the addition of a methyl group to DNA catalyzed by DNA methyltransferases (DNMTs), resulting in the conversion of the cytosine to 5’-methylcytosine, where the cytosine residue is followed by a guanine residue (CpG). In mammals, there are unusually dense clusters of CpG dinucleotides in the promoters of genes which are called “CpG islands”. For many housekeeping genes, these CpG islands are unmethylated in normal healthy tissue. However, CpG islands methylation is often associated with gene silencing and changed patterns of DNA methylation, associated with altered patterns of gene transcription, contribute to the aetiology of several diseases and to ageing. The overall aim of this project was to characterise the changes in DNA methylation which are observed during cell senescence (in the human cell line MRC-5) and during ageing and in response to dietary energy restriction (in various mice tissues). For this purpose: i) global DNA methylation was quantified using various techniques (ELISA, LINE-1, B1 and LUMA assays and HPLC); ii) site-specific genome-wide screening for methylation changes was performed using the MeDIP technique followed by hybridization to DNA microarrays; iii) validation of the results for selected candidates was performed by pyrosequencing and iv) investigation of mitochondrial DNA methylation was conducted using bisulphite-modified-DNA PCR direct sequencing. Effects of senescence on gene expression were assessed by transcriptome microarrays and by RT-qPCR studies. During the study, previously established methods for the investigation of global DNA methylation (LUMA) and site-specific methylation (MeDIP) were improved. Whilst global DNA methylation changes were detectable in senescence and after short-term dietary energy restriction, DNMTs/Dnmts expression changes were observed in senescence, ageing and following dietary energy restriction and were tissue- and treatment-specific. In parallel, site-specific aberrant DNA methylation was found in the promoters of the genes CTTN, GLIPR2, NPTX1 and SLC39A14 in replicative senescent MRC-5 human fibroblasts. These changes were validated by pyrosequencing and were accompanied by changes in expression of the corresponding genes. Also, in a pilot study, promoter methylation of several cell cycle genes was altered in replicative senescence with associated changes in gene expression. Concordant methylation changes were found in the promoters of 47 gene in ageing mouse and heart tissues including the genes Wnt5a, Map4k5, Apcdd1, Chp2 and Rasgrp2. In addition, dietary energy restriction counteracted the age-related DNA methylation changes in the promoters of 40 genes, including Aifm1, Irf8, Rarg, Nmi, Maf1, Rab33a and Fxn in mouse liver. Finally, mitochondrial DNA methylation studies revealed that senescence affected the DNA methylation patterns of the MT-COI and MT-ND1 gene coding sequences in MRC-5 fibroblasts whilst ageing affected the DNA methylation pattern of the D-Loop region in mouse liver, but this was not reversed by dietary energy restriction. Pathway analysis revealed that senescence- and age-related aberrant DNA methylation affected genes involved in inter-cellular communication, stress response, malignant transformation, cellular development/proliferation control, cell growth/differentiation and survival, apoptosis and immune response. As these genes contribute to the maintenance of cellular and tissue homeostasis, these findings suggest a potential role for altered DNA methylation in the aetiology of senescence and ageing. On the other hand, short-term dietary energy restriction modulated some of the age-related aberrant DNA methylation patterns of the ageing mouse liver, in particular those in promoters of genes involved in apoptosis regulation, inflammatory and immune response to viral infections, transcription regulation, vesicle trafficking and mitochondrial iron transport and respiration. Finally, mitochondrial DNA aberrant methylation - found to occur at genes belonging to Complex IV and to Complex I - may contribute to the accumulation of hazardous superoxide species in senescent cells whilst DNA aberrant methylation at the D-Loop mitochondrial regulatory region may contribute to age-related mitochondrial dysfunction. In conclusion, these findings suggest that altered DNA methylation may have a role in the aetiology of senescence and ageing and that some of the effects of dietary energy restriction in slowing down the ageing process and also delaying the onset of age-related diseases may occur via epigenetic mechanisms, including amelioration of age-related aberrations in patterns of DNA methylation

New approaches to unveil the Transcriptional landscape of dopaminergic neurons

Recent advances in studying the mammalian transcriptome arised new questions about how genes are organized and what is the function of noncoding RNAs. Furthermore, the discovery of large amounts of polyA- transcripts and antisense transcription proved that a portion of the transcriptome has still to be characterized. The complex anatomo-functional organization of the brain has prevented a comprehensive analysis of the transcriptional landscape of this tissue. New techniques must be developed to approach neuronal heterogeneity. In this study we combined Laser Capture Microdissection (LCM) and nanoCAGE, based on Cap Analysis of Gene Expression (CAGE), to describe expressed genes and map their transcription start sites (TSS) in two specific populations, A9 and A10, of mouse mesencephalic dopaminergic cells. Although sharing common dopaminergic marker genes, these two populations are part of different midbrain anatomical structures, substantia nigra (SN) for A9 and ventral tegmental area (VTA) for A10, project to relatively distinct areas, participate to distinct ascending dopaminergic pathways, exhibit different electrophysiological properties and different susceptibility to neurodegeneration in Parkinson`s disease. Specific neurons were identified by the expression of Green Fluorescent Protein driven by a celltype specific promoter in transgenic mice. High-quality RNAs were purified from 1000-2500 cells collected by LCM. We adapted the CAGE technique to analyze limiting amounts of RNAs (nanoCAGE). We took advantage of the cap-switching properties of the reverse transcriptase to specifically tag the 5`end of transcripts with a sequence containing a class III restriction site for EcoP15I. By creating 32bp 5`tags, we considerably improved the TSS mapping rate on the genome. A semi-suppressive PCR strategy was used to prevent primer dimers formation. The use of random priming in the 1st strand synthesis allowed to capture poly(A)- RNAs. 5`tags were sequenced with Illumina-Solexa platform. Here we show that this new nanoCAGE technology ensures a true high-throughput coverage of the transcriptome of a small number of identified neurons and can be used as an effective mean for gene discovery in the noncoding RNAs, to uncover putative alternative promoters associated to variants of protein coding transcripts and to detect potentially regulatory antisense transcripts. A further experimental validation by 5`RACE (Rapid Amplification of cDNA Ends) and RT-PCR on few candidate genes, have confirmed the existence in vivo of alternative TSS in the case of key regulatory genes involved in specifying and maintaining the dopaminergic phenotype of these neurons such as \u3b1-synuclein (Snca), dopamine transporter (Dat), vescicular monoamine transporter 2 (Vmat2), catechol-O-methyltransferase (Comt). Furthermore the differential expression of an antisense transcript overlapping to the polyubiquitin (Ubc) gene was detected as potentially interesting candidate gene accounting for differences in the ubiquitin-proteasome system (UPS) function in the two neuron populations. The potential implications deriving from these newly discovered alternative promoters and transcripts are discussed, considering also the potential consequences for the corresponding protein isoforms