MOTIVATION: High-throughput sequencing technologies place ever increasing demands on existing algorithms for sequence analysis. Algorithms for computing maximal exact matches (MEMs) between sequences appear in two contexts where high-throughput sequencing will vastly increase the volume of sequence data: (i) seeding alignments of high-throughput reads for genome assembly and (ii) designating anchor points for genome-genome comparisons. RESULTS: We introduce a new algorithm for finding MEMs. The algorithm leverages a sparse suffix array (SA), a text index that stores every K-th position of the text. In contrast to a full text index that stores every position of the text, a sparse SA occupies much less memory. Even though we use a sparse index, the output of our algorithm is the same as a full text index algorithm as long as the space between the indexed suffixes is not greater than a minimum length of a MEM. By relying on partial matches and additional text scanning between indexed positions, the algorithm trades memory for extra computation. The reduced memory usage makes it possible to determine MEMs between significantly longer sequences. AVAILABILITY: Source code for the algorithm is available under a BSD open source license at http://compbio.cs.princeton.edu/mems. The implementation can serve as a drop-in replacement for the MEMs algorithm in MUMmer 3.

Genome maps are crucial tools in human genetic research, providing known landmarks for locating disease genes and frameworks for large-scale sequencing.Radiation hybrid mapping is one technique for building genome maps.In this paper, we describe the methods used to build radiation hybrid maps of the entire human genome.We present the hidden Markov model that we employ to estimate the likelihood of a map despite uncertainty about the data, and we discuss the problem of searching for maximum-likelihood maps.We describe the graph algorithms used to find sparse but reliable init,ial maps and our methods of extending them.Finally, we show results validating our software on simulated data, and we describe our genome-wide human radiat,ion hybrid maps and the evidence supporting them.

We show that a simple statistical mechanics model can capture the collective behavior of large networks of spiking neurons. Qualitative arguments suggest that regularly firing neurons should be described by a planar "spin" of unit length. We extract these spins from spike trains and then measure the interaction Hamiltonian using simulations of small clusters of cells. Correlations among spike trains obtained from simulations of large arrays of cells are in quantitative agreement with the predictions from these Hamiltonians. We comment on the novel computational abilities of these "XY networks."

I demonstrate that it is possible to construct a three-layer network of the standard feedforward architecture that can solve the N input-N output encoder problem with just two hidden units.