Methods for alignment of protein sequences typically measure similait by using a substitution matrix with scores for all possible exchanges of one amino acid with another. The most widely used matrices are based on the Dayhoff model of evolutionary rates. Using a different approach, we have derived substitution matrices from about 2000 blocks of aligned sequence segments characterizing more than 500 groups of related proteins. This led to marked improvements in alignments and in searches using queries from each of the groups.Among the most useful computer-based tools in modem biology are those that involve sequence alignments of proteins, since these alignments often provide important insights into gene and protein function. There are several different types of alignments: global alignments of pairs of proteins related by common ancestry throughout their lengths, local alignments involving related segments of proteins, multiple alignments of members of protein families, and alignments made during data base searches to detect homology. In each case, competing alignments are evaluated by using a scoring scheme for estimating similarity. Although several different scoring schemes have been proposed (1-6), the mutation data matrices of Dayhoff (1, 7-9) are generally considered the standard and are often the default in alignment and searching programs. In the Dayhoff model, substitution rates are derived from alignments of protein sequences that are at least 85% identical. However, the most common task involving substitution matrices is the detection of much more distant relationships, which are only inferred from substitution rates in the Dayhoff model. Therefore, we wondered whether a better approach might be to use alignments in which these relationships are explicitly represented. An incentive for investigating this possibility is that implementation of an improved matrix in numerous important applications requires only trivial effort. METHODSDeriving a Frequency Table from a Data Base of Blocks. Local alignments can be represented as ungapped blocks with each row a different protein segment and each column an aligned residue position. Previously, we described an automated system, PROTOMAT, for obtaining a set of blocks given a group of related proteins (10). This system was applied to a catalog of several hundred protein groups, yielding a data base of >2000 blocks. Consider a single block representing a conserved region of a protein family. For a new member of this family, we seek a set of scores for matches and mismatches that best favors a correct alignment with each of the other segments in the block relative to an incorrect alignment. For each column of the block, we first count the number of matches and mismatches of each type between the new sequence and every other sequence in the block. For example, if the residue of the new sequence that aligns with the first column of the first block is A and the column has 9 A residues and 1 S residue, then there are 9 AA matches and 1 AS mismatch. This proce...

Many chromatin features play critical roles in regulating gene expression. A complete understanding of gene regulation will require the mapping of specific chromatin features in small samples of cells at high resolution. Here we describe Cleavage Under Targets and Tagmentation (CUT&Tag), an enzyme-tethering strategy that provides efficient high-resolution sequencing libraries for profiling diverse chromatin components. In CUT&Tag, a chromatin protein is bound in situ by a specific antibody, which then tethers a protein A-Tn5 transposase fusion protein. Activation of the transposase efficiently generates fragment libraries with high resolution and exceptionally low background. All steps from live cells to sequencing-ready libraries can be performed in a single tube on the benchtop or a microwell in a high-throughput pipeline, and the entire procedure can be performed in one day. We demonstrate the utility of CUT&Tag by profiling histone modifications, RNA Polymerase II and transcription factors on low cell numbers and single cells.

To gain insight into how genomic information is translated into cellular and developmental programs, the Drosophila model organism Encyclopedia of DNA Elements (modENCODE) project is comprehensively mapping transcripts, histone modifications, chromosomal proteins, transcription factors, replication proteins and intermediates, and nucleosome properties across a developmental time course and in multiple cell lines. We have generated more than 700 data sets and discovered protein-coding, noncoding, RNA regulatory, replication, and chromatin elements, more than tripling the annotated portion of the Drosophila genome. Correlated activity patterns of these elements reveal a functional regulatory network, which predicts putative new functions for genes, reveals stage- and tissue-specific regulators, and enables gene-expression prediction. Our results provide a foundation for directed experimental and computational studies in Drosophila and related species and also a model for systematic data integration toward comprehensive genomic and functional annotation.