Biomedical subjects

Vineet Bafna

Publications and source records attributed to Vineet Bafna.

26 records · Page 2Linked to original sources

PubMed2004

Optimal haplotype block-free selection of tagging SNPs for genome-wide association studies.

It is widely hoped that the study of sequence variation in the human genome will provide a means of elucidating the genetic component of complex diseases and variable drug responses. A major stumbling block to the successful design and execution of genome-wide disease association studies using single-nucleotide polymorphisms (SNPs) and linkage disequilibrium is the enormous number of SNPs in the human genome. This results in unacceptably high costs for exhaustive genotyping and presents a challenging problem of statistical inference. Here, we present a new method for optimally selecting minimum informative subsets of SNPs, also known as "tagging" SNPs, that is efficient for genome-wide selection. We contrast this method to published methods including haplotype block tagging, that is, grouping SNPs into segments of low haplotype diversity and typing a subset of the SNPs that can discriminate all common haplotypes within the blocks. Because our method does not rely on a predefined haplotype block structure and makes use of the weaker correlations that occur across neighboring blocks, it can be effectively applied across chromosomal regions with both high and low local linkage disequilibrium. We show that the number of tagging SNPs selected is substantially smaller than previously reported using block-based approaches and that selecting tagging SNPs optimally can result in a two- to threefold savings over selecting random SNPs.

Algorithms↗

PubMed2004

FastR: fast database search tool for non-coding RNA.

The discovery of novel non-coding RNAs has been among the most exciting recent developments in Biology. Yet, many more remain undiscovered. It has been hypothesized that there is in fact an abundance of functional non-coding RNA (ncRNA) with various catalytic and regulatory functions. Computational methods tailored specifically for ncRNA are being actively developed. As the inherent signal for ncRNA is weaker than that for protein coding genes, comparative methods offer the most promising approach, and are the subject of our research. We consider the following problem: Given an RNA sequence with a known secondary structure, efficiently compute all structural homologs (computed as a function of sequence and structural similarity) in a genomic database. Our approach, based on structural filters that eliminate a large portion of the database, while retaining the true homologs allows us to search a typical bacterial database in minutes on a standard PC, with high sensitivity and specificity. This is two orders of magnitude better than current available software for the problem.

Algorithms↗

PubMed2003

The dog genome: survey sequencing and comparative analysis.

A survey of the dog genome sequence (6.22 million sequence reads; 1.5x coverage) demonstrates the power of sample sequencing for comparative analysis of mammalian genomes and the generation of species-specific resources. More than 650 million base pairs (>25%) of dog sequence align uniquely to the human genome, including fragments of putative orthologs for 18,473 of 24,567 annotated human genes. Mutation rates, conserved synteny, repeat content, and phylogeny can be compared among human, mouse, and dog. A variety of polymorphic elements are identified that will be valuable for mapping the genetic basis of diseases and traits in the dog.

Animals↗

PubMed2003

Haplotyping as perfect phylogeny: a direct approach.

A full haplotype map of the human genome will prove extremely valuable as it will be used in large-scale screens of populations to associate specific haplotypes with specific complex genetic-influenced diseases. A haplotype map project has been announced by NIH. The biological key to that project is the surprising fact that some human genomic DNA can be partitioned into long blocks where genetic recombination has been rare, leading to strikingly fewer distinct haplotypes in the population than previously expected (Helmuth, 2001; Daly et al., 2001; Stephens et al., 2001; Friss et al., 2001). In this paper we explore the algorithmic implications of the no-recombination in long blocks observation, for the problem of inferring haplotypes in populations. This assumption, together with the standard population-genetic assumption of infinite sites, motivates a model of haplotype evolution where the haplotypes in a population are assumed to evolve along a coalescent, which as a rooted tree is a perfect phylogeny. We consider the following algorithmic problem, called the perfect phylogeny haplotyping problem (PPH), which was introduced by Gusfield (2002) - given n genotypes of length m each, does there exist a set of at most 2n haplotypes such that each genotype is generated by a pair of haplotypes from this set, and such that this set can be derived on a perfect phylogeny? The approach taken by Gusfield (2002) to solve this problem reduces it to established, deep results and algorithms from matroid and graph theory. Although that reduction is quite simple and the resulting algorithm nearly optimal in speed, taken as a whole that approach is quite involved, and in particular, challenging to program. Moreover, anyone wishing to fully establish, by reading existing literature, the correctness of the entire algorithm would need to read several deep and difficult papers in graph and matroid theory. However, as stated by Gusfield (2002), many simplifications are possible and the list of "future work" in Gusfield (2002) began with the task of developing a simpler, more direct, yet still efficient algorithm. This paper accomplishes that goal, for both the rooted and unrooted PPH problems. It establishes a simple, easy-to-program, O(nm(2))-time algorithm that determines whether there is a PPH solution for input genotypes and produces a linear-space data structure to represent all of the solutions. The approach allows complete, self-contained proofs. In addition to algorithmic simplicity, the approach here makes the representation of all solutions more intuitive than in Gusfield (2002), and solves another goal from that paper, namely, to prove a nontrivial upper bound on the number of PPH solutions, showing that that number is vastly smaller than the number of haplotype solutions (each solution being a set of n pairs of haplotypes that can generate the genotypes) when the perfect phylogeny requirement is not imposed.

Algorithms↗

PubMed2003

Robustness of inference of haplotype block structure.

In this report, we examine the validity of the haplotype block concept by comparing block decompositions derived from public data sets by variants of several leading methods of block detection. We first develop a statistical method for assessing the concordance of two block decompositions. We then assess the robustness of inferred haplotype blocks to the specific detection method chosen, to arbitrary choices made in the block-detection algorithms, and to the sample analyzed. Although the block decompositions show levels of concordance that are very unlikely by chance, the absolute magnitude of the concordance may be low enough to limit the utility of the inference. For purposes of SNP selection, it seems likely that methods that do not arbitrarily impose block boundaries among correlated SNPs might perform better than block-based methods.

Algorithms↗

PubMedDate not supplied

Peptide sequence tags for fast database search in mass-spectrometry.

Filtration techniques in the form of rapid elimination of candidate sequences while retaining the true one are key ingredients of database searches in genomics. Although SEQUEST and Mascot perform a conceptually similar task to the tool BLAST, the key algorithmic idea of BLAST (filtration) was never implemented in these tools. As a result MS/MS protein identification tools are becoming too time-consuming for many applications including search for post-translationally modified peptides. Moreover, matching millions of spectra against all known proteins will soon make these tools too slow in the same way that "genome vs genome" comparisons instantly made BLAST too slow. We describe the development of filters for MS/MS database searches that dramatically reduce the running time and effectively remove the bottlenecks in searching the huge space of protein modifications. Our approach, based on a probability model for determining the accuracy of sequence tags, achieves superior results compared to GutenTag, a popular tag generation algorithm. Our tag generating algorithm along with our de novo sequencing algorithm PepNovo can be accessed via the URL http://peptide.ucsd.edu/.

Algorithms↗

PubMedDate not supplied

The number of recombination events in a sample history: conflict graph and lower bounds.

We consider the following problem: Given a set of binary sequences, determine lower bounds on the minimum number of recombinations required to explain the history of the sample, under the infinite-sites model of mutation. The problem has implications for finding recombination hotspots and for the Ancestral Recombination Graph reconstruction problem. Hudson and Kaplan gave a lower bound based on the four-gamete test. In practice, their bound Rm often greatly underestimates the minimum number of recombinations. The problem was recently revisited by Myers and Griffiths, who introduced two new lower bounds Rh and Rs which are provably better, and also yield good bounds in practice. However, the worst-case complexities of their procedures for computing Rh and Rs are exponential and super-exponential, respectively. In this paper, we show that the number of nontrivial connected components, Rc, in the conflict graph for a given set of sequences, computable in time O(nm2), is also a lower bound on the minimum number of recombination events. We show that in many cases, Rc is a better bound than Rh. The conflict graph was used by Gusfield et al. to obtain a polynomial time algorithm for the galled tree problem, which is a special case of the Ancestral Recombination Graph (ARG) reconstruction problem. Our results also offer some insight into the structural properties of this graph and are of interest for the general Ancestral Recombination Graph reconstruction problem.

Alcohol Dehydrogenase↗

PubMedDate not supplied

Searching genomes for noncoding RNA using FastR.

The discovery of novel noncoding RNAs has been among the most exciting recent developments in biology. It has been hypothesized that there is, in fact, an abundance of functional noncoding RNAs (ncRNAs) with various catalytic and regulatory functions. However, the inherent signal for ncRNA is weaker than the signal for protein coding genes, making these harder to identify. We consider the following problem: Given an RNA sequence with a known secondary structure, efficiently detect all structural homologs in a genomic database by computing the sequence and structure similarity to the query. Our approach, based on structural filters that eliminate a large portion of the database while retaining the true homologs, allows us to search a typical bacterial genome in minutes on a standard PC. The results are two orders of magnitude better than the currently available software for the problem. We applied FastR to the discovery of novel riboswitches, which are a class of RNA domains found in the untranslated regions. They are of interest because they regulate metabolite synthesis by directly binding metabolites. We searched all available eubacterial and archaeal genomes for riboswitches from purine, lysine, thiamin, and riboflavin subfamilies. Our results point to a number of novel candidates for each of these subfamilies and include genomes that were not known to contain riboswitches.

Algorithms↗