Some intriguing high-throughput DNA sequence variants prediction over protein functionality

This paper intends to review computational methods and high throughput automated tools for precisely prediction various functionalities of uncharacterized proteins based on their desired DNA sequence information alone. Then proposes a hybrid weighted network and Genetic Algorithm to improve prediction purpose. The main advantage of the method is the protein function and DNA sequence prediction can be computed precisely using best fitness parent in genetic algorithm. With the accomplishment of human genome sequencing, the number of sequence-known proteins has increased exponentially and the pace is much slower in determining their biological attributes. The gap between DNA sequence variants and their functionalities has become increasingly large. However, detection of sequences based on protein data bank has become benchmark for many researchers. As amount of DNA sequence data continues to increase, the fundamental problem stay at the front of genome analysis. In the course of developing these methods, the following matters were often needed to consider: benchmark dataset construction, gene sequence prediction, operating algorithm, anticipated accuracy, gene recommender and functional integrations. In this review, we are to discuss each of them, with a different focus on operational algorithms and how to increase the accuracy of DNA sequence variants predictio

CHILD: a new tool for detecting low-abundance insertions and deletions in standard sequence traces

Several methods have been proposed for detecting insertion/deletions (indels) from chromatograms generated by Sanger sequencing. However, most such methods are unsuitable when the mutated and normal variants occur at unequal ratios, such as is expected to be the case in cancer, with organellar DNA or with alternatively spliced RNAs. In addition, the current methods do not provide robust estimates of the statistical confidence of their results, and the sensitivity of this approach has not been rigorously evaluated. Here, we present CHILD, a tool specifically designed for indel detection in mixtures where one variant is rare. CHILD makes use of standard sequence alignment statistics to evaluate the significance of the results. The sensitivity of CHILD was tested by sequencing controlled mixtures of deleted and undeleted plasmids at various ratios. Our results indicate that CHILD can identify deleted molecules present as just 5% of the mixture. Notably, the results were plasmid/primer-specific; for some primers and/or plasmids, the deleted molecule was only detected when it comprised 10% or more of the mixture. The false positive rate was estimated to be lower than 0.4%. CHILD was implemented as a user-oriented web site, providing a sensitive and experimentally validated method for the detection of rare indel-carrying molecules in common Sanger sequence reads

Decoding of Superimposed Traces Produced by Direct Sequencing of Heterozygous Indels

Direct Sanger sequencing of a diploid template containing a heterozygous insertion or deletion results in a difficult-to-interpret mixed trace formed by two allelic traces superimposed onto each other. Existing computational methods for deconvolution of such traces require knowledge of a reference sequence or the availability of both direct and reverse mixed sequences of the same template. We describe a simple yet accurate method, which uses dynamic programming optimization to predict superimposed allelic sequences solely from a string of letters representing peaks within an individual mixed trace. We used the method to decode 104 human traces (mean length 294 bp) containing heterozygous indels 5 to 30 bp with a mean of 99.1% bases per allelic sequence reconstructed correctly and unambiguously. Simulations with artificial sequences have demonstrated that the method yields accurate reconstructions when (1) the allelic sequences forming the mixed trace are sufficiently similar, (2) the analyzed fragment is significantly longer than the indel, and (3) multiple indels, if present, are well-spaced. Because these conditions occur in most encountered DNA sequences, the method is widely applicable. It is available as a free Web application Indelligent at http://ctap.inhs.uiuc.edu/dmitriev/indel.asp

AutoCSA, an algorithm for high throughput DNA sequence variant detection in cancer genomes

The undertaking of large-scale DNA sequencing screens for somatic variants in human cancers requires accurate and rapid processing of traces for variants. Due to their often aneuploid nature and admixed normal tissue, heterozygous variants found in primary cancers are often subtle and difficult to detect. To address these issues, we have developed a mutation detection algorithm, AutoCSA, specifically optimized for the high throughput screening of cancer samples