We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the logexpectation score, and refinement using treedependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.

MUSCLE: multiple sequence alignment with high accuracy and high throughput

The newest version of MUMmer easily handles comparisons of large eukaryotic genomes at varying evolutionary distances, as demonstrated by applications to multiple genomes. Two new graphical viewing tools provide alternative ways to analyze genome alignments. The new system is the first version of MUMmer to be released as open-source software. This allows other developers to contribute to the code base and freely redistribute the code. The MUMmer sources are available at http://www.tigr.org/software/mummer.

/pdf/versatile-and-open-software-for-comparing-large-genomes-25wj2yhhq7.pdf

Versatile and open software for comparing large genomes

We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view of chromatin structure has emerged, including its inter-relationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded new mechanistic and evolutionary insights concerning the functional landscape of the human genome. Together, these studies are defining a path for pursuit of a more comprehensive characterization of human genome function.

/pdf/identification-and-analysis-of-functional-elements-in-1-of-2o640l8ikl.pdf

Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project

As genomes evolve, they undergo large-scale evolutionary processes that present a challenge to sequence comparison not posed by short sequences. Recombination causes frequent genome rearrangements, horizontal transfer introduces new sequences into bacterial chromosomes, and deletions remove segments of the genome. Consequently, each genome is a mosaic of unique lineage-specific segments, regions shared with a subset of other genomes and segments conserved among all the genomes under consideration. Furthermore, the linear order of these segments may be shuffled among genomes. We present methods for identification and alignment of conserved genomic DNA in the presence of rearrangements and horizontal transfer. Our methods have been implemented in a software package called Mauve. Mauve has been applied to align nine enterobacterial genomes and to determine global rearrangement structure in three mammalian genomes. We have evaluated the quality of Mauve alignments and drawn comparison to other methods through extensive simulations of genome evolution.

/pdf/mauve-multiple-alignment-of-conserved-genomic-sequence-with-5f3czjsw6r.pdf

Mauve: multiple alignment of conserved genomic sequence with rearrangements.

We have conducted a comprehensive search for conserved elements in vertebrate genomes, using genome-wide multiple alignments of five vertebrate species (human, mouse, rat, chicken, and Fugu rubripes). Parallel searches have been performed with multiple alignments of four insect species (three species of Drosophila and Anopheles gambiae), two species of Caenorhabditis, and seven species of Saccharomyces. Conserved elements were identified with a computer program called phastCons, which is based on a two-state phylogenetic hidden Markov model (phylo-HMM). PhastCons works by fitting a phylo-HMM to the data by maximum likelihood, subject to constraints designed to calibrate the model across species groups, and then predicting conserved elements based on this model. The predicted elements cover roughly 3%-8% of the human genome (depending on the details of the calibration procedure) and substantially higher fractions of the more compact Drosophila melanogaster (37%-53%), Caenorhabditis elegans (18%-37%), and Saccharaomyces cerevisiae (47%-68%) genomes. From yeasts to vertebrates, in order of increasing genome size and general biological complexity, increasing fractions of conserved bases are found to lie outside of the exons of known protein-coding genes. In all groups, the most highly conserved elements (HCEs), by log-odds score, are hundreds or thousands of bases long. These elements share certain properties with ultraconserved elements, but they tend to be longer and less perfectly conserved, and they overlap genes of somewhat different functional categories. In vertebrates, HCEs are associated with the 3' UTRs of regulatory genes, stable gene deserts, and megabase-sized regions rich in moderately conserved noncoding sequences. Noncoding HCEs also show strong statistical evidence of an enrichment for RNA secondary structure.

https://faculty.washington.edu/wjs18/Phylo_Shadow-footprint/PhastCons.pdf

Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes

Comparing genomic sequences across related species is a fruitful source of biological insight, because functional elements such as exons tend to exhibit significant sequence similarity, whereas regions that are not functional tend to be less conserved. The first step in comparing genomic sequences is to align them—that is, to map the letters of one sequence to those of the others. There are several categories of alignments: local alignments that identify local similarities between regions of each sequence, and global alignments that find a monotonically increasing map between the letters of each sequence; pairwise alignments that compare two sequences, and multiple alignments that compare several sequences.

Local pairwise alignment methods such as Smith-Waterman (1981), BLAST (Altschul et al. 1990, 1997), BLASTZ (Schwartz et al. 2000), SSAHA (Ning et al. 2001), and BLAT (Kent 2002) are able to pinpoint locations of rearrangements between two sequences, and are suitable for aligning draft sequences or individual reads. Global alignments are important because they reveal the shared order of biological features in the compared species, and produce a more accurate alignment at the base-pair level when the features are in the same order. The best-known global alignment algorithm is Needleman-Wunsch (1970), which requires time proportional to the product of the lengths of the aligned sequences. Unfortunately this algorithm is too inefficient for comparing long genomic sequences. Faster methods have been developed recently: DIALIGN (Morgenstern et al. 1998, Brudno and Morgenstern 2002), MUMmer (Delcher et al. 1999, 2002), GLASS (Batzoglou et al. 2000), WABA (Kent and Zahler 2000), and AVID (Bray et al. 2003). Most of these methods have proven effective in aligning genomic sequences from two closely related organisms, such as human and mouse or Caenorhabditis elegans and C. briggsae, but have not been tested in alignments between distant relatives such as human and fugu.

Multiple alignments, a natural extension of two-sequence comparisons, are a powerful way to study biological sequences. Even weak similarity across several sequences usually reveals an important conserved biological feature (Dubchak et al. 2000; Gottgens et al. 2002). Moreover, multiple alignments enable the computation of local rates of evolution, giving a quantitative measure of the strength of evolutionary constraints and the functional importance of local regions (Simon et al. 2002). Multiple alignments are considerably more difficult to compute than are pairwise alignments: the running time scales as the product of the lengths of all the sequences. Formally, the problem is NP-complete (Wang and Jiang 1994; Bonizzoni and Vedova 2001). For this reason heuristic approaches are usually applied, of which the most widely used is progressive alignment, which constructs a multiple alignment by successive applications of a pairwise alignment algorithm. The best-known system based on progressive alignment is perhaps CLUSTALW (Thompson et al. 1994). Some other systems include MULTALIGN (Barton and Sternberg 1987), MULTAL (Taylor 1988), YAMA (Hardison et al. 1993, 1994), and PRRP (Gotoh 1996). DIALIGN (Morgenstern 1999) does not use progressive alignment; instead it uses another heuristic approach to chain local conserved blocks between several sequences into a multiple alignment. These systems can effectively align proteins and relatively short genomic regions, but are not efficient enough to align entire genomes. MGA (Hohl et al. 2002) is a rapid multiple aligner suitable for comparing very close homologs, such as different strains of a bacterium, but is not designed to align distant homologs.

Here we describe novel systems for pairwise and multiple alignment of genomic sequences: LAGAN (Limited Area Global Alignment of Nucleotides), an efficient and reliable pairwise aligner that is suitable for genomic comparison of distantly related organisms, and MLAGAN (Multi-LAGAN), a multiple aligner based on progressive alignment with LAGAN. We tested our systems on sequence from 12 species generated for the genomic segment harboring the cystic fibrosis transmembrane conductance regulator (CFTR) gene (J.W. Thomas, J.W. Touchman, R.W. Blakesley, G.G. Bouffard, S.M. Beckstrom-Sternberg, E.H. Margulies, M. Blanchette, A.C. Siepel, P.J. Thomas, J.C. McDowell, B. Maskeri, N.F. Hansen, M.S. Schwartz, R.J. Weber, W.J. Kent, D. Karolchik, T.C. Bruen, R. Bevan, D.J. Cutler, S. Schwartz, L. Elnitski, J.R. Idol, A.B. Prasad, S.-Q. Lee-Lin, V.V.B. Maduro, M.E. Portnoy, N.L. Dietrich, N. Akhter, K. Ayele, B. Benjamin, K. Cariaga, C.P. Brinkley, S.Y. Brooks, S. Granite, X. Guan, J. Gupta, P. Haghighi, S-L. Ho, M.C. Huang, E. Karlins, P.L. Laric, R. Legaspi, M.J. Lim, Q.L. Maduro, C.A. Masiello, S.D. Mastrian, J.C. McCloskey, R. Pearson, S. Stantripop, E.E. Tiongson, J.T. Tran, C. Tsurgeon, J.L. Vogt, M.A. Walker, K.D. Wetherby, L.S. Wiggins, A.C. Young, L-H. Zhang, K. Osoegawa, B. Zhu, B. Zhao, C.L. Shu, P.J. De Jong, C.E. Lawrence, A.F. Smit, A. Chakravarti, D. Haussler, P. Green, W. Miller, and E.D. Green, in prep.). Based on comparisons with other available alignment programs and benchmarking on standard desktop computer systems, we conclude that LAGAN and MLAGAN are practical and reliable methods for large-scale pairwise and multiple genomic alignment that should prove useful for obtaining alignments of the entire human, mouse, fugu, rat, and other genomes in the context of a whole-genome alignment pipeline.

/pdf/lagan-and-multi-lagan-efficient-tools-for-large-scale-1xbpy7jg4a.pdf

LAGAN and Multi-LAGAN: efficient tools for large-scale multiple alignment of genomic DNA.

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LAGAN and Multi-LAGAN: efficient tools for large-scale multiple alignment of genomic DNA.