Evidence-Based Detection of Pancreatic Canc

This study is an effort to develop a tool for early detection of pancreatic cancer using evidential reasoning. An evidential reasoning model predicts the likelihood of an individual developing pancreatic cancer by processing the outputs of a Support Vector Classifier, and other input factors such as smoking history, drinking history, sequencing reads, biopsy location, family and personal health history. Certain features of the genomic data along with the mutated gene sequence of pancreatic cancer patients was obtained from the National Cancer Institute (NIH) Genomic Data Commons (GDC). This data was used to train the SVC. A prediction accuracy of ~85% with a ROC AUC of 83.4% was achieved. Synthetic data was assembled in different combinations to evaluate the working of evidential reasoning model. Using this, variations in the belief interval of developing pancreatic cancer are observed. When the model is provided with an input of high smoking history and family history of cancer, an increase in the evidential reasoning interval in belief of pancreatic cancer and support in the machine learning model prediction is observed. Likewise, decrease in the quantity of genetic material and an irregularity in the cellular structure near the pancreas increases support in the machine learning classifier’s prediction of having pancreatic cancer. This evidence-based approach is an attempt to diagnose the pancreatic cancer at a premalignant stage. Future work includes using the real sequencing reads as well as accurate habits and real medical and family history of individuals to increase the efficiency of the evidential reasoning model. Next steps also involve trying out different machine learning models to observe their performance on the dataset considered in this study

Toward Early Detection Of Pancreatic Cancer: An Evidence-Based Approach

This study observes how an evidential reasoning approach can be used as a diagnostic tool for early detection of pancreatic cancer. The evidential reasoning model combines the output of a linear Support Vector Classifier (SVC) with factors such as smoking history, health history, biopsy location, NGS technology used, and more to predict the likelihood of the disease. The SVC was trained using genomic data of pancreatic cancer patients derived from the National Cancer Institute (NIH) Genomic Data Commons (GDC). To test the evidential reasoning model, a variety of synthetic data was compiled to test the impact of combinations of different factors. Through experimentation, we monitored how the evidential interval for pancreatic cancer fluctuated based on the inputs that were provided. We observed how the pancreatic cancer evidential interval increased and the machine learning prediction of pancreatic cancer was supported when the input changed from a non-smoker and non-drinker to an individual with a highly active smoking and drinking history. Similarly, we observed how the evidential interval for pancreatic cancer increased significantly when the machine learning prediction for pancreatic cancer was maintained as high and the input of the quality of the sequencing read was changed from a high quantity of cytosine guanine content and homopolymer regions to a moderate quantity of cytosine guanine content and low homopolymer regions; indicating that there was initially a higher likelihood of error in the sequencing reads, resulting in a more inaccurate machine learning output. This experiment shows that an evidence-based approach has the potential to contribute as a diagnostic tool for screening for high-risk groups. Future work should focus on improving the machine learning model by using a larger pancreatic cancer genomic database. Next steps will involve programmatically analyzing real sequencing reads for irregular guanine cytosine content and high homopolymer regions

Computational methods to improve genome assembly and gene prediction

DNA sequencing is used to read the nucleotides composing the genetic material that forms individual organisms. As 2nd generation sequencing technologies offering high throughput at a feasible cost have matured, sequencing has permeated nearly all areas of biological research. By a combination of large-scale projects led by consortiums and smaller endeavors led by individual labs, the flood of sequencing data will continue, which should provide major insights into how genomes produce physical characteristics, including disease, and evolve. To realize this potential, computer science is required to develop the bioinformatics pipelines to efficiently and accurately process and analyze the data from large and noisy datasets. Here, I focus on two crucial bioinformatics applications: the assembly of a genome from sequencing reads and protein-coding gene prediction. In genome assembly, we form large contiguous genomic sequences from the short sequence fragments generated by current machines. Starting from the raw sequences, we developed software called Quake that corrects sequencing errors more accurately than previous programs by using coverage of k-mers and probabilistic modeling of sequencing errors. My experiments show correcting errors with Quake improves genome assembly and leads to the detection of more polymorphisms in re-sequencing studies. For post-assembly analysis, we designed a method to detect a particular type of mis-assembly where the two copies of each chromosome in diploid genomes diverge. We found thousands of examples in each of the chimpanzee, cow, and chicken public genome assemblies that created false segmental duplications. Shotgun sequencing of environmental DNA (often called metagenomics) has shown tremendous potential to both discover unknown microbes and explore complex environments. We developed software called Scimm that clusters metagenomic sequences based on composition in an unsupervised fashion more accurately than previous approaches. Finally, we extended an approach for predicting protein-coding genes on whole genomes to metagenomic sequences by adding new discriminative features and augmenting the task with taxonomic classification and clustering of the sequences. The program, called Glimmer-MG, predicts genes more accurately than all previous methods. By adding a model for sequencing errors that also allows the program to predict insertions and deletions, accuracy significantly improves on error-prone sequences

PERGA: A Paired-End Read Guided De Novo Assembler for Extending Contigs Using SVM and Look Ahead Approach

Since the read lengths of high throughput sequencing (HTS) technologies are short, de novo assembly which plays significant roles in many applications remains a great challenge. Most of the state-of-the-art approaches base on de Bruijn graph strategy and overlap-layout strategy. However, these approaches which depend on k-mers or read overlaps do not fully utilize information of paired-end and single-end reads when resolving branches. Since they treat all single-end reads with overlapped length larger than a fix threshold equally, they fail to use the more confident long overlapped reads for assembling and mix up with the relative short overlapped reads. Moreover, these approaches have not been special designed for handling tandem repeats (repeats occur adjacently in the genome) and they usually break down the contigs near the tandem repeats. We present PERGA (Paired-End Reads Guided Assembler), a novel sequence-reads-guided de novo assembly approach, which adopts greedy-like prediction strategy for assembling reads to contigs and scaffolds using paired-end reads and different read overlap size ranging from Omax to Omin to resolve the gaps and branches. By constructing a decision model using machine learning approach based on branch features, PERGA can determine the correct extension in 99.7% of cases. When the correct extension cannot be determined, PERGA will try to extend the contig by all feasible extensions and determine the correct extension by using look-ahead approach. Many difficult-resolved branches are due to tandem repeats which are close in the genome. PERGA detects such different copies of the repeats to resolve the branches to make the extension much longer and more accurate. We evaluated PERGA on both Illumina real and simulated datasets ranging from small bacterial genomes to large human chromosome, and it constructed longer and more accurate contigs and scaffolds than other state-of-the-art assemblers. PERGA can be freely downloaded at https://github.com/hitbio/PERGA.published_or_final_versio

Metagenomics : tools and insights for analyzing next-generation sequencing data derived from biodiversity studies

Advances in next-generation sequencing (NGS) have allowed significant breakthroughs in microbial ecology studies. This has led to the rapid expansion of research in the field and the establishment of “metagenomics”, often defined as the analysis of DNA from microbial communities in environmental samples without prior need for culturing. Many metagenomics statistical/computational tools and databases have been developed in order to allow the exploitation of the huge influx of data. In this review article, we provide an overview of the sequencing technologies and how they are uniquely suited to various types of metagenomic studies. We focus on the currently available bioinformatics techniques, tools, and methodologies for performing each individual step of a typical metagenomic dataset analysis. We also provide future trends in the field with respect to tools and technologies currently under development. Moreover, we discuss data management, distribution, and integration tools that are capable of performing comparative metagenomic analyses of multiple datasets using well-established databases, as well as commonly used annotation standards

The Expanding Landscape of Alternative Splicing Variation in Human Populations.

Alternative splicing is a tightly regulated biological process by which the number of gene products for any given gene can be greatly expanded. Genomic variants in splicing regulatory sequences can disrupt splicing and cause disease. Recent developments in sequencing technologies and computational biology have allowed researchers to investigate alternative splicing at an unprecedented scale and resolution. Population-scale transcriptome studies have revealed many naturally occurring genetic variants that modulate alternative splicing and consequently influence phenotypic variability and disease susceptibility in human populations. Innovations in experimental and computational tools such as massively parallel reporter assays and deep learning have enabled the rapid screening of genomic variants for their causal impacts on splicing. In this review, we describe technological advances that have greatly increased the speed and scale at which discoveries are made about the genetic variation of alternative splicing. We summarize major findings from population transcriptomic studies of alternative splicing and discuss the implications of these findings for human genetics and medicine

High-resolution microbial community reconstruction by integrating short reads from multiple 16S rRNA regions

The emergence of massively parallel sequencing technology has revolutionized microbial profiling, allowing the unprecedented comparison of microbial diversity across time and space in a wide range of host-associated and environmental ecosystems. Although the high-throughput nature of such methods enables the detection of low-frequency bacteria, these advances come at the cost of sequencing read length, limiting the phylogenetic resolution possible by current methods. Here, we present a generic approach for integrating short reads from large genomic regions, thus enabling phylogenetic resolution far exceeding current methods. The approach is based on a mapping to a statistical model that is later solved as a constrained optimization problem. We demonstrate the utility of this method by analyzing human saliva and Drosophila samples, using Illumina single-end sequencing of a 750 bp amplicon of the 16S rRNA gene. Phylogenetic resolution is significantly extended while reducing the number of falsely detected bacteria, as compared with standard single-region Roche 454 Pyrosequencing. Our approach can be seamlessly applied to simultaneous sequencing of multiple genes providing a higher resolution view of the composition and activity of complex microbial communities

Bioinformatics and computational tools for next-generation sequencing analysis in clinical genetics

Clinical genetics has an important role in the healthcare system to provide a definitive diagnosis for many rare syndromes. It also can have an influence over genetics prevention, disease prognosis and assisting the selection of the best options of care/treatment for patients. Next-generation sequencing (NGS) has transformed clinical genetics making possible to analyze hundreds of genes at an unprecedented speed and at a lower price when comparing to conventional Sanger sequencing. Despite the growing literature concerning NGS in a clinical setting, this review aims to fill the gap that exists among (bio)informaticians, molecular geneticists and clinicians, by presenting a general overview of the NGS technology and workflow. First, we will review the current NGS platforms, focusing on the two main platforms Illumina and Ion Torrent, and discussing the major strong points and weaknesses intrinsic to each platform. Next, the NGS analytical bioinformatic pipelines are dissected, giving some emphasis to the algorithms commonly used to generate process data and to analyze sequence variants. Finally, the main challenges around NGS bioinformatics are placed in perspective for future developments. Even with the huge achievements made in NGS technology and bioinformatics, further improvements in bioinformatic algorithms are still required to deal with complex and genetically heterogeneous disorders

Analytical Tools and Databases for Metagenomics in the Next-Generation Sequencing Era

Metagenomics has become one of the indispensable tools in microbial ecology for the last few decades, and a new revolution in metagenomic studies is now about to begin, with the help of recent advances of sequencing techniques. The massive data production and substantial cost reduction in next-generation sequencing have led to the rapid growth of metagenomic research both quantitatively and qualitatively. It is evident that metagenomics will be a standard tool for studying the diversity and function of microbes in the near future, as fingerprinting methods did previously. As the speed of data accumulation is accelerating, bioinformatic tools and associated databases for handling those datasets have become more urgent and necessary. To facilitate the bioinformatics analysis of metagenomic data, we review some recent tools and databases that are used widely in this field and give insights into the current challenges and future of metagenomics from a bioinformatics perspective.