At Washington University in St. Louis, Gish revolutionized similarity searching by developing the first BLAST suite of programs to combine rapid gapped sequence alignment with statistical evaluation methods appropriate for gapped alignment scores. The resulting search programs were significantly more sensitive but only marginally slower than ungapped BLAST, due to novel application of the BLAST dropoff score X during gapped alignment extension. Sensitivity of gapped BLAST was further improved by the novel application of Karlin-Altschul Sum statistics to the evaluation of multiple, gapped alignment scores in all BLAST search modes. Sum statistics were originally developed analytically for the evaluation of multiple, ungapped alignment scores. The empirical use of Sum statistics in the treatment of gapped alignment scores was validated in collaboration with Stephen Altschul, from 1994-1995.
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Then, Biegert and Söding compared the sequence window to a library with thousands of context profiles. The library is generated by clustering a representative set of sequence profile windows. The actual predicting of mutation probabilities is achieved by weighted mixing of the central columns of the most similar context profiles [4]. This aligns short profiles that are nonhomologous and ungapped which gives higher weight to better matching profiles, making them easier to detect [4].
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In the field of unconventional superconductivity, a Fermi arc is a phenomenon visible in the pseudogap state of a superconductor. Seen in momentum space, part of the space exhibits a gap in the density of states, like in a superconductor. This starts at the antinodal points, and spreads through momentum space when lowering the temperature until everywhere is gapped and the sample is superconducting. The area in momentum space that remains ungapped is called the Fermi Arc.
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SENSEI, however, is more efficient than the other methods, but is incompetent in other forms of alignment as its strength lies in handling ungapped alignments. The quality of the production from Megablast, on the other hand, is of poor quality and does not adapt well to large sequences. Techniques such as MUMmer and QUASAR employ suffix trees, which are supposed to handle exact matches. However, these methods can only apply to the comparison of sequences that display elevated similarities.
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Electrons traveling at 45° to this bond can move freely throughout the crystal. The Fermi surface therefore consists of Fermi arcs forming pockets centered on the corner of the Brillouin zone. In the pseudogap phase these arcs gradually disappear as the temperature is lowered until only four points on the diagonals of the Brillouin zone remain ungapped. On one hand, this could indicate a completely new electronic phase which consumes available states, leaving only a few to pair up and superconduct.
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In particular, this corrects zero-probability entries in the matrix to values that are small but nonzero. Blocks analysis is a method of motif finding that restricts motifs to ungapped regions in the alignment. Blocks can be generated from an MSA or they can be extracted from unaligned sequences using a precalculated set of common motifs previously generated from known gene families. Block scoring generally relies on the spacing of high-frequency characters rather than on the calculation of an explicit substitution matrix.
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In May 1996, WU-BLAST version 2.0 with gapped alignments was publicly released in the form of a drop-in upgrade for existing users of ungapped NCBI BLAST and WU-BLAST (both at version 1.4, after having forked in 1994). Little NIH funding was received for his WU-BLAST development, with an average of 20% FTE starting in November 1995, and ending shortly after the September 1997 release of the NCBI gapped BLAST (“blastall”). As an option to WU-BLAST, Gish implemented a faster, more memory-efficient and more sensitive two-hit BLAST algorithm than was used by the NCBI software for many years.
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MaMF, or Mammalian Motif Finder, is an algorithm for identifying motifs to which transcription factors bind. The algorithm takes as input a set of promoter sequences, and a motif width(w), and as output, produces a ranked list of 30 predicted motifs(each motif is defined by a set of N sequences, where N is a parameter). The algorithm firstly indexes each sub-sequence of length n, where n is a parameter around 4-6 base pairs, in each promoter, so they can be looked up efficiently. This index is then used to build a list of all pairs of sequences of length w, such that each sequence shares an n-mer, and each sequence forms an ungapped alignment with a substring of length w from the string of length 2w around the match, with a score exceeding a cut- off.
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