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Biomedical subjects

Evan Eichler

Publications and source records attributed to Evan Eichler.

4 recordsLinked to original sources
PubMed2006

Human chromosome 11 DNA sequence and analysis including novel gene identification.

Chromosome 11, although average in size, is one of the most gene- and disease-rich chromosomes in the human genome. Initial gene annotation indicates an average gene density of 11.6 genes per megabase, including 1,524 protein-coding genes, some of which were identified using novel methods, and 765 pseudogenes. One-quarter of the protein-coding genes shows overlap with other genes. Of the 856 olfactory receptor genes in the human genome, more than 40% are located in 28 single- and multi-gene clusters along this chromosome. Out of the 171 disorders currently attributed to the chromosome, 86 remain for which the underlying molecular basis is not yet known, including several mendelian traits, cancer and susceptibility loci. The high-quality data presented here--nearly 134.5 million base pairs representing 99.8% coverage of the euchromatic sequence--provide scientists with a solid foundation for understanding the genetic basis of these disorders and other biological phenomena.

Chromosomes, Human, Pair 11↗
PubMed2004

Identifying uniformly mutated segments within repeats.

Given a long string of characters from a constant size alphabet we present an algorithm to determine whether its characters have been generated by a single i.i.d. random source. More specifically, consider all possible n-coin models for generating a binary string S, where each bit of S is generated via an independent toss of one of the n coins in the model. The choice of which coin to toss is decided by a random walk on the set of coins where the probability of a coin change is much lower than the probability of using the same coin repeatedly. We present a procedure to evaluate the likelihood of a n-coin model for given S, subject a uniform prior distribution over the parameters of the model (that represent mutation rates and probabilities of copying events). In the absence of detailed prior knowledge of these parameters, the algorithm can be used to determine whether the a posteriori probability for n=1 is higher than for any other n>1. Our algorithm runs in time O(l4logl), where l is the length of S, through a dynamic programming approach which exploits the assumed convexity of the a posteriori probability for n. Our test can be used in the analysis of long alignments between pairs of genomic sequences in a number of ways. For example, functional regions in genome sequences exhibit much lower mutation rates than non-functional regions. Because our test provides means for determining variations in the mutation rate, it may be used to distinguish functional regions from non-functional ones. Another application is in determining whether two highly similar, thus evolutionarily related, genome segments are the result of a single copy event or of a complex series of copy events. This is particularly an issue in evolutionary studies of genome regions rich with repeat segments (especially tandemly repeated segments).

Algorithms↗
PubMed2002

ViewGene: a graphical tool for polymorphism visualization and characterization.

The human genome project is producing an enormous amount of sequence data, based on which single base changes between individuals can be identified. Unfortunately, computer tools that were adequate for sequence assembly are less than ideal for the characterization of polymorphism data [single nucleotide (snp) or insertion/deletion (indel)] and other sequence features, and their relationship to each other. We have developed viewGene as a flexible tool that takes input from a number of sequence formats and analysis programs (Genbank, FASTA, RepeatMasker, Cross match, BLAST, user-defined data) to construct a sequence reference scaffold that can be viewed through a simple graphical interface. polymorphisms generated from many sources can be added to this scaffold through the same sequence formats, with a variety of options to control what is displayed. Large amounts of polymorphism data can be organized so that patterns and haplotypes can be readily discerned. In our laboratory, viewGene has been used to view annotated genbank records, find nonrepetitive sequence fragments for polymorphism detection, and visualize similarity search results. Manipulation, cross-referencing, and haplotype viewing of snp data are essential for quality assessment and identification of variants associated with genetic disease, and viewGene provides all three of these important functions.

Amino Acid Sequence↗