Algorithm for DNA copy number variation detection with read depth and paramorphism information

Next-generation sequencing (NGS) has revolutionized the detection of structural variation in genome. Among NGS strategies, read depth is widely used and paramorphism information contained inside is generally ignored. We develop an algorithm that can fully exploit both read depth and paramorphism information. We embed mutation procedure in our system model for estimating prior likelihood of single nucleotide base. Hidden Markov model (HMM) is used to connect single base into segments and belief propagation algorithm is performed for the optimal solution of the HMM model. Simulations show promising results in detecting important types of structural variation. We have applied the algorithm on the maize B73 and MO17 genome data and compared the results with those obtained from array CGH method based micro-array data. Inconsistency between the two sets of data is discussed

Graphical model and algorithm for detecting DNA structural variation

Next-generation sequencing (NGS) has revolutionized the detection of structural variation in genome. Among NGS strategies, reading depth is widely used and paramorphism information contained inside is generally ignored. We develop an algorithm that can fully exploit both reading depth and paramorphism information. We embed mutation procedure in our system model for estimating prior likelihood of single nucleotide base. Hidden Markov model is used to connect single base into segments and belief propagation algorithm is performed for the optimal solution of the HMM model. Simulations show promising results in detecting important types of structural variation. We have applied the algorithm on the maize B73 and MO17 genome data and compared the results with those obtained from arrayCGH method based micro-array data. Inconsistency between the two sets of data is discussed

Assembly and analysis of complex plant genomes

Concurrent advances in high-throughput sequencing and assembly have led to the completion of many complex genomes. Even so, these assemblies require substantial computational resources. In this dissertation, we present a massively parallel approach that scales to thousands of processors without duplicating the biological expertise present in conventional assembly software.;Additional bioinformatics techniques were required to accurately assemble the maize genome including novel repeat detection, and the resulting framework has been strongly supported by maize experimental data. More recently, this framework has been generalized for fruit fly, sorghum, soybean and environmental sequence assemblies.;Questions in plant genome analysis were also addressed. For example, we have discovered an estimated 350 orphan maize genes and have shown that approximately 1% of all maize genes were recently duplicated, many of which into at least two functional copies. LCM-454 sequencing is introduced and analyses that indicate this approach can discover rare, potentially tissue-specific transcripts and thousands of SNPs will be presented.;This dissertation combines high performance computing, computational biology and high-throughput sequencing for our ongoing work on the maize genome project. We conclude by describing how these contributions can be useful for any species, including non-model organisms that are unlikely to be fully sequenced

Algorithm for DNA copy number variation detection with read depth and paramorphism information

Next-generation sequencing (NGS) has revolutionized the detection of structural variation in genome. Among NGS strategies, read depth is widely used and paramorphism information contained inside is generally ignored. We develop an algorithm that can fully exploit both read depth and paramorphism information. We embed mutation procedure in our system model for estimating prior likelihood of single nucleotide base. Hidden Markov model (HMM) is used to connect single base into segments and belief propagation algorithm is performed for the optimal solution of the HMM model. Simulations show promising results in detecting important types of structural variation. We have applied the algorithm on the maize B73 and MO17 genome data and compared the results with those obtained from array CGH method based micro-array data. Inconsistency between the two sets of data is discussed.This proceeding is published as Shen, Rong, Kai Ying, Zhengdao Wang, and Patrick S. Schnable. "Algorithm for DNA copy number variation detection with read depth and paramorphism information." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). (2016): 869-873. DOI: 10.1109/ICASSP.2016.7471799.</p

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence