000114837 001__ 114837
000114837 005__ 20190527151552.0
000114837 0247_ $$2doi$$a10.1073/pnas.89.6.2002
000114837 037__ $$aARTICLE
000114837 245__ $$aMethods and algorithms for statistical analysis of protein sequences
000114837 269__ $$a1992
000114837 260__ $$c1992
000114837 336__ $$aJournal Articles
000114837 500__ $$aDepartment of Mathematics, Stanford University, CA 94305-2125.
000114837 520__ $$aWe describe several protein sequence statistics designed to evaluate distinctive attributes of residue content and arrangement in primary structure. Considered are global compositional biases, local clustering of different residue types (e.g., charged residues, hydrophobic residues, Ser/Thr), long runs of charged or uncharged residues, periodic patterns, counts and distribution of homooligopeptides, and unusual spacings between particular residue types. The computer program SAPS (statistical analysis of protein sequences) calculates all the statistics for any individual protein sequence input and is available for the UNIX environment through electronic mail on request to V.B. (volker/genomic@stanford.edu).
000114837 700__ $$aBrendel, V.
000114837 700__ $$0244404$$g113607$$aBucher, P.
000114837 700__ $$aNourbakhsh, I. R.
000114837 700__ $$aBlaisdell, B. E.
000114837 700__ $$aKarlin, S.
000114837 773__ $$j89$$tProceedings Of The National Academy Of Sciences Of The United States Of America$$k6$$q2002-6
000114837 909C0 $$xU11780$$0252244$$pGR-BUCHER
000114837 909CO $$pSV$$particle$$ooai:infoscience.tind.io:114837
000114837 937__ $$aGR-BUCHER-ARTICLE-1992-001
000114837 973__ $$rREVIEWED$$sPUBLISHED$$aOTHER
000114837 980__ $$aARTICLE