Utilization of Probabilistic Models in Short Read Assembly from Second-Generation Sequencing

With the advent of cheaper and faster DNA sequencing technologies, assembly methods have greatly changed. Instead of outputting reads that are thousands of base pairs long, new sequencers parallelize the task by producing read lengths between 35 and 400 base pairs. Reconstructing an organism’s genome from these millions of reads is a computationally expensive task. Our algorithm solves this problem by organizing and indexing the reads using n-grams, which are short, fixed-length DNA sequences of length n. These n-grams are used to efficiently locate putative read joins, thereby eliminating the need to perform an exhaustive search over all possible read pairs. Our goal was develop a novel n-gram method for the assembly of genomes from next-generation sequencers. Specifically, a probabilistic, iterative approach was utilized to determine the most likely reads to join through development of a new metric that models the probability of any two arbitrary reads being joined together. Tests were run using simulated short read data based on randomly created genomes ranging in lengths from 10,000 to 100,000 nucleotides with 16 to 20x coverage. We were able to successfully re-assemble entire genomes up to 100,000 nucleotides in length.

Crystal structure of a CAP-DNA complex: the DNA is bent by 90 degrees

The 3 angstrom resolution crystal structure of the Escherichia coli catabolite gene activator protein (CAP) complexed with a 30-base pair DNA sequence shows that the DNA is bent by 900. This bend results almost entirely from two 400 kinks that occur between TG/CA base pairs at positions 5 and 6 on each side of the dyad axis of the complex. DNA sequence discrimination by CAP derives both from sequence-dependent distortion of the DNA helix and from direct hydrogen-bonding interactions between three protein side chains and the exposed edges of three base pairs in the major groove of the DNA. The structure of this transcription factor-DNA complex provides insights into possible mechanisms of transcription activation