| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Published online 19 March 2004
Nucleic Acids Research, 2004, Vol. 32, No. 5 1792-1797
Oxford University Press
MUSCLE: multiple sequence alignment with high accuracy and high throughput
195 Roque Moraes Drive, Mill Valley, CA 94941, USA
*Email: bob{at}drive5.com
Received January 19, 2004; Revised January 30, 2004; Accepted February 24, 2004
| ABSTRACT |
|---|
|
|
|---|
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 log-expectation score, and refinement using tree-dependent 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.
| INTRODUCTION |
|---|
|
|
|---|
Multiple alignments of protein sequences are important in many applications, including phylogenetic tree estimation, structure prediction and critical residue identification. The most natural formulation of the computational problem is to define a model of sequence evolution that assigns probabilities to elementary sequence edits and seeks a most probable directed graph in which edges represent edits and terminal nodes are the observed sequences. No tractable method for finding such a graph is known. A heuristic alternative is to seek a multiple alignment that optimizes the sum of pairs (SP) score, i.e. the sum of pairwise alignment scores. Optimizing the SP score is NP complete (1) and can be achieved by dynamic programming with time and space complexity O(LN) in the sequence length L and number of sequences N (2). A more popular strategy is the progressive method (3,4), which first estimates a tree and then constructs a pairwise alignment of the subtrees found at each internal node. A subtree is represented by its profile, a multiple alignment treated as a sequence by regarding each column as an alignable symbol. A variant on this strategy is used by T-Coffee (5), which aligns profiles by optimizing a score derived from local and global alignments of all pairs of input sequences. Misalignments by progressive methods are sometimes readily apparent (Fig. 1), motivating further processing (refinement). For a recent review of multiple alignment methods, see Notredame (6). Here we describe MUSCLE (multiple sequence comparison by log-expectation), a new computer program for multiple protein sequence alignment.
|
| MUSCLE algorithm |
|---|
|
|
|---|
Here we give an overview of the algorithm; a more detailed discussion is given in Edgar (submitted). Following guide tree construction, the fundamental step is pairwise profile alignment, which is used first for progressive alignment and then for refinement. This is similar to the strategies used by PRRP (7) and MAFFT (8).
Distance measures and guide tree estimation
MUSCLE uses two distance measures for a pair of sequences: a kmer distance (for an unaligned pair) and the Kimura distance (for an aligned pair). A kmer is a contiguous subsequence of length k, also known as a word or k-tuple. Related sequences tend to have more kmers in common than expected by chance. The kmer distance is derived from the fraction of kmers in common in a compressed alphabet, which we have previously shown to correlate well with fractional identity (9). This measure does not require an alignment, giving a significant speed advantage. Given an aligned pair of sequences, we compute the pairwise identity and convert to an additive distance estimate, applying the Kimura correction for multiple substitutions at a single site (10). Distance matrices are clustered using UPGMA (11), which we find to give slightly improved results over neighbor-joining (12), despite the expectation that neighbor-joining will give a more reliable estimate of the evolutionary tree. This can be explained by assuming that in progressive alignment, the best accuracy is obtained at each node by aligning the two profiles that have fewest differences, even if they are not evolutionary neighbors.
Profile alignment
In order to apply pairwise alignment to profiles, a scoring function must be defined on an aligned pair of profile positions, i.e. a pair of multiple alignment columns [see, for example Edgar and Sjolander (13)]. Let i and j be amino acid types, pi the background probability of i, pij the joint probability of i and j being aligned to each other, fxi the observed frequency of i in column x of the first profile, and f xG the observed frequency of gaps in that column at position x in the family (similarly for position y in the second profile). The estimated probability
xi of observing amino acid i in position x can be derived from fx, typically by adding heuristic pseudo-counts or by using Bayesian methods such as Dirichlet mixture priors (14). MUSCLE uses a new profile function we call the log-expectation (LE) score:
LExy = (1 f xG) (1 f yG) log
i
j f xi f yj pij/pi pj1
This is a modified version of the log-average function (15):
LAxy = log
i
j
xi
yj pij/pi pj2
MUSCLE uses probabilities pi and pij derived from the 240 PAM VTML matrix (16). Frequencies fi are normalized to sum to 1 when indels are present (otherwise the logarithm becomes increasingly negative with increasing numbers of gaps even when aligning conserved or similar residues). The factor (1 fG) is the occupancy of a column, introduced to encourage more highly occupied columns to align. Position-specific gap penalties are used, employing heuristics similar to those found in MAFFT and LAGAN (17).
Algorithm
The high-level flow is depicted in Figure 2.
|
Stage 1, Draft progressive. The goal of the first stage is to produce a multiple alignment, emphasizing speed over accuracy.
1.1 The kmer distance is computed for each pair of input sequences, giving distance matrix D1.
1.2 Matrix D1 is clustered by UPGMA, producing binary tree TREE1.
1.3 A progressive alignment is constructed by following the branching order of TREE1. At each leaf, a profile is constructed from an input sequence. Nodes in the tree are visited in prefix order (children before their parent). At each internal node, a pairwise alignment is constructed of the two child profiles, giving a new profile which is assigned to that node. This produces a multiple alignment of all input sequences, MSA1, at the root.
Stage 2, Improved progressive. The main source of error in the draft progressive stage is the approximate kmer distance measure, which results in a suboptimal tree. MUSCLE therefore re-estimates the tree using the Kimura distance, which is more accurate but requires an alignment.
2.1 The Kimura distance for each pair of input sequences is computed from MSA1, giving distance matrix D2.
2.2 Matrix D2 is clustered by UPGMA, producing binary tree TREE2.
2.3 A progressive alignment is produced following TREE2 (similar to 1.3), producing multiple alignment MSA2. This is optimized by computing alignments only for subtrees whose branching orders changed relative to TREE1.
Stage 3, Refinement.
3.1 An edge is chosen from TREE2 (edges are visited in order of decreasing distance from the root).
3.2 TREE2 is divided into two subtrees by deleting the edge. The profile of the multiple alignment in each subtree is computed.
3.3 A new multiple alignment is produced by re-aligning the two profiles.
3.4 If the SP score is improved, the new alignment is kept, otherwise it is discarded.
Steps 3.13.4 are repeated until convergence or until a user-defined limit is reached. This is a variant of tree-dependent restricted partitioning (18).
Complete multiple alignments are available at steps 1.3, 2.3 and 3.4, at which points the algorithm may be terminated. We refer to the first two stages alone as MUSCLE-p, which produces MSA2. MUSCLE-p has time complexity O(N2L + NL2) and space complexity O(N2 + NL + L2). Refinement adds an O(N3L) term to the time complexity.
| Assessment |
|---|
|
|
|---|
We assessed the performance of MUSCLE on four sets of reference alignments: BAliBASE (19,20), SABmark (21), SMART (2224) and a new benchmark, PREFAB. We compared these with four other methods: CLUSTALW (25), probably the most widely used program at the time of writing; T-Coffee, which has the best BAliBASE score reported to date; and two MAFFT scripts: FFTNS1, the fastest previously published method known to the author (in which diagonal finding by fast Fourier transform is enabled and a progressive alignment constructed), and NWNSI, the slowest but most accurate of the MAFFT methods (in which fast Fourier transform is disabled and refinement is enabled). Tested versions were MUSCLE 3.2, CLUSTALW 1.82, T-Coffee 1.37 and MAFFT 3.82. We also evaluated MUSCLE-p, in which the refinement stage is omitted. We also tried Align-m 1.0 (21), but found in many cases that the program either aborted or was impractically slow on the larger alignments found in SMART and PREFAB.
BAliBASE. We used version 2 of the BAliBASE benchmark, reference sets Ref 1Ref 5. Other reference sets contain repeats, inversions and transmembrane helices, for which none of the tested algorithms is designed.
SABmark. We used version 1.63 of the SABmark reference alignments, which consists of two subsets: Superfamily and Twilight. All sequences have known structure. The Twilight set contains 1994 domains from the Astral database (26) with pairwise sequence similarity e-values
1, divided into 236 folds according to the SCOP classification (27). The Superfamily set contains sequences of pairwise identity
50%, divided into 462 SCOP superfamilies. Each pair of structures was aligned with two structural aligners: SOFI (28) and CE (29), producing a sequence alignment from the consensus in which only high-confidence regions are retained. Input sets range from three to 25 sequences, with an average of eight and an average sequence length of 179.
SMART. SMART contains multiple alignments refined by experts, focusing primarily on signaling domains. While structures were considered where known, sequence methods were also used to aid construction of the database, so SMART is not suitable as a definitive benchmark. However, conventional wisdom [e.g. Fischer et al. (30)] holds that machine-assisted experts can produce superior alignments to automated methods, so performance on this set is of interest for comparison. We used a version of SMART downloaded in July 2000, before the first version of MUSCLE was made available; eliminating the possibility that MUSCLE was used to aid construction. We discarded alignments of more than 100 sequences in order to make the test tractable for T-Coffee, leaving 267 alignments averaging 31 sequences of length 175.
PREFAB. The methods used to create databases such as BAliBASE and SMART are time-consuming and demand significant expertise, making a fully automated protocol desirable. Perhaps the most obvious approach is to generate sequence alignments from automated alignments of multiple structures, but this is fraught with difficulties; see for example Eidhammer et al. (31). With this in mind, we constructed a new test set, PREFAB (protein reference alignment benchmark) which exploits methodology (21,32,33), test data (13,34,35) and statistical methods (19) that have previously been applied to alignment accuracy assessment. The protocol is as follows. Two proteins are aligned by a structural method that does not incorporate sequence similarity. Each sequence is used to query a database, from which high-scoring hits are collected. The queries and their hits are combined and aligned by a multiple sequence method. Accuracy is assessed on the original pair alone, by comparison with their structural alignment. Three test sets selected from the FSSP database (36) were used as described in Sadreyev and Grishin (34) (data kindly provided by Ruslan Sadreyev), and Edgar and Sjolander (13,35), which we call SG, PP1 and PP2, respectively. These three sets vary mainly in their selection criteria. PP1 and PP2 contain pairs with sequence identity
30%. PP1 was designed to select pairs that have high structural similarity, requiring a z-score of
15 and a root mean square deviation (r.m.s.d.) of
2.5 Å. PP2 selected more diverged pairs with a z-score of
8 and
12, and an r.m.s.d. of
3.5 Å. SG contains pairs sampled from three ranges of sequence identity: 015, 1530 and 3097%, with no z-score or r.m.s.d. limits. We re-aligned each pair of structures using the CE aligner (29), and retained only those pairs for which FSSP and CE agreed on 50 or more positions. This was designed to minimize questionable and ambiguous structural alignments as done in SABmark and MaxBench (33). We used the full-chain sequence of each structure to make a PSI-BLAST (37,38) search of the NCBI non-redundant protein sequence database (39), keeping locally aligned regions of hits with e-values below 0.01. Hits were filtered to 80% maximum identity (including the query), and 24 selected at random. Finally, each pair of structures and their remaining hits were combined to make sets of
50 sequences. The limit of 50 was arbitrarily chosen to make the test tractable on a desktop computer for some of the more resource-intensive methods, in particular T-Coffee (which needed 10 CPU days, as noted in Table 4). The final set, PREFAB version 3.0, has 1932 alignments averaging 49 sequences of length 240, of which 178 positions in the structure pair are found in the consensus of FSSP and CE.
|
Accuracy measurement
We used three accuracy measures: Q, TC and APDB. Q (quality) is the number of correctly aligned residue pairs divided by the number of residue pairs in the reference alignment. This has previously been termed the developer score (32) and SPS (40). TC (total column score) is the number of correctly aligned columns divided by the number of columns in the reference alignment; this is Thompson et al.s CS and is equivalent to Q in the case of two sequences (as in PREFAB). APDB (41) is derived from structures alone; no reference alignment of the sequences or structures is needed. For BAliBASE, we use Q and TC, measured only on core blocks as annotated in the database. For PREFAB, we use Q, including only those positions on which CE and FSSP agree, and also APDB. For SMART, we use Q and TC computed for all columns. For SABmark, we average the Q score over each pair of sequences. TC score is not applicable to SABmark as the reference alignments are pairwise.
Statistical analysis
Following Thompson et al. (19), statistical significance is measured by a Friedman rank test (42), which is more conservative than the Wilcoxon test that has also been used for alignment accuracy discrimination (5,7,8) as fewer assumptions are made about the population distribution. In particular, the Wilcoxon test assumes a symmetrical difference between two methods, but in practice we sometimes observe a significant skew. PREFAB and SABmark use automated structure alignment methods, which sometimes produce questionable results. Many low-quality regions are eliminated by taking the consensus between two independent aligners, but some may remain. In PREFAB, assessment of a multiple alignment is made on a single pair of sequences, which may be more or less accurately aligned than the average over all pairs. In SABmark, the upper bound on Q is less than 1 to a varying degree because the pairwise reference alignments may not be mutually consistent. These effects can be viewed as introducing noise into the experiment, and a single accuracy measurement may be subject to error. However, as the structural aligners do not use primary sequence, these errors are unbiased with respect to sequence methods. A difference in accuracy between two sequence alignment methods can therefore be established by the Friedman test, and the measured difference in average accuracy will be approximately correct when measured over a sufficient number of samples.
| RESULTS |
|---|
|
|
|---|
Quality scores and CPU times are summarized in Tables 1234567; rankings and statistical significance on PREFAB and BAliBASE for all pairs of methods are given in Table 8. On all test sets and quality measures, MUSCLE achieves the highest ranking (in some cases jointly with other methods due to lack of statistical significance), and MUSCLE-p is statistically indistinguishable from T-Coffee and NWNSI. MUSCLE achieves the highest BAliBASE score reported to date, but the improvement of 1.6% in Q and 2.2% in TC over T-Coffee has low significance (P = 0.15). A similar result is found on SABmark, where MUSCLE achieves a 1.5% improvement over T-Coffee in Q with P = 0.14. The Q score on PREFAB is best able to distinguish between methods, giving statistically significant rankings to MUSCLE > MUSCLE-p, MUSCLE > T-Coffee, MUSCLE > NWNSI and MUSCLE-p > NWNSI. SMART also ranks MUSCLE highest. SMART cannot be considered definitive due to the use of sequence methods in construction of the database, although any bias from this source is likely to favor methods that were available to the SMART developers (i.e. to be against MUSCLE). The SMART results could be interpreted as suggesting that MUSCLE alignments are more consistent with refinements made by human experts. The APDB score appears to be relatively insensitive, showing no significant improvement due to the refinement stage of MUSCLE (similarly for MAFFT; not shown), and is not able to distinguish between the four highest scoring methods. We speculate that the scatter observed in the correlation between APDB and more conventional measures such as TC (40) injects sufficient noise to obscure meaningful differences in accuracy that can be resolved using Q. The low rank of Align-m on SABmark differs from results quoted by Van Walle et al. (21), who assessed pairwise alignments produced by an intermediate step in the algorithm, whereas we used the final multiple alignment.
|
|
|
|
|
|
|
Resource requirements for large numbers of sequences
To investigate resource requirements for increasing number of sequences N, we used the Rose sequence generator (43) (complete results not shown). In agreement with other studies, [e.g. Katoh et al. (8)], we found that T-Coffee was unable to align more than approximately 102 sequences of typical length on a current desktop computer. CLUSTALW was able to align a few hundred sequences, with a practical limit around N = 103 where CPU time begins to scale approximately as N4. The largest set had 5000 sequences of average length 350. MUSCLE-p completed this test in 7 min, compared with 10 min for FFTNS1; we estimate that CLUSTALW would need approximately 1 year.
| DISCUSSION |
|---|
|
|
|---|
We have described a new multiple sequence alignment algorithm, MUSCLE, and presented evidence that it creates alignments with average accuracy comparable with or superior to the best current methods. It should be emphasized that performance differences between the better methods emerge only when averaged over a large number of test cases, even when alignments are considered trustworthy. For example, on BAliBASE, the lowest scoring of the tested methods (FFTNS1) achieved a higher Q than the highest scoring (MUSCLE) in 21 out of 141 alignments and tied in 19 more; compared with T-Coffee, MUSCLE scored higher or tied in 95 cases, but lower in 24. This suggests the use of multiple algorithms and careful inspection of the results. MUSCLE is comparable in speed with CLUSTALW, completing a test set (PREFAB) averaging 49 sequences of length 240 in about half the time. The progressive method MUSCLE-p, which has average accuracy statistically indistinguishable from T-Coffee and the most accurate MAFFT script, is the fastest algorithm known to the author for large numbers of sequences, able to align 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE software, source code and test data are freely available at: http://www.drive5. com/muscle.
| REFERENCES |
|---|
|
|
|---|
- Wang,L. and Jiang,T. (1994) On the complexity of multiple sequence alignment. J. Comput. Biol., 1, 337348.[Medline]
- Waterman,M.S., Smith,T.F. and Beyer,W.A. (1976) Some biological sequence metrics. Adv. Math., 20, 367387.[CrossRef]
- Hogeweg,P. and Hesper,B. (1984) The alignment of sets of sequences and the construction of phyletic trees: an integrated method. J. Mol. Evol., 20, 175186.[CrossRef][Web of Science][Medline]
- Feng,D.F. and Doolittle,R.F. (1987) Progressive sequence alignment as a prerequisite to correct phylogenetic trees. J. Mol. Evol., 25, 351360.[Web of Science][Medline]
- Notredame,C., Higgins,D.G. and Heringa,J. (2000) T-Coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol., 302, 205217.[CrossRef][Web of Science][Medline]
- Notredame,C. (2002) Recent progress in multiple sequence alignment: a survey. Pharmacogenomics, 3, 131144.[CrossRef][Web of Science][Medline]
- Gotoh,O. (1996) Significant improvement in accuracy of multiple protein sequence alignments by iterative refinement as assessed by reference to structural alignments. J. Mol. Biol., 264, 823838.[CrossRef][Web of Science][Medline]
- Katoh,K., Misawa,K., Kuma,K. and Miyata,T. (2002) MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res., 30, 30593066.
[Abstract/Free Full Text] - Edgar,R.C. (2004) Local homology recognition and distance measures in linear time using compressed amino acid alphabets. Nucleic Acids Res., 32, 380385.
[Abstract/Free Full Text] - Kimura,M. (1983) The Neutral Theory of Molecular Evolution. Cambridge University Press.
- Sneath,P.H.A. and Sokal,R.R. (1973) Numerical Taxonomy. Freeman, San Francisco.
- Saitou,N. and Nei,M. (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol., 4, 406425.[Abstract]
- Edgar,R.C. and Sjolander,K. (2004) A comparison of scoring functions for protein sequence profile alignment. Bioinformatics, DOI: 10.1093/bioinformatics/bth090.
- Sjolander,K., Karplus,K., Brown,M., Hughey,R., Krogh,A., Mian,I.S. and Haussler,D. (1996) Dirichlet mixtures: a method for improved detection of weak but significant protein sequence homology. CABIOS, 12, 327345.
- von Ohsen,N. and Zimmer,R. (2001) Improving profileprofile alignment via log average scoring. In Gascuel,O. and Moret,B.M.E. (eds), Algorithms in Bioinformatics, First International Workshop, WABI 2001. Springer-Verlag, Berlin, Germany, pp. 1126.
- Muller,T., Spang,R. and Vingron,M. (2002) Estimating amino acid substitution models: a comparison of Dayhoffs estimator, the resolvent approach and a maximum likelihood method. Mol. Biol. Evol., 19, 813.
[Abstract/Free Full Text] - Brudno,M., Do,C.B., Cooper,G.M., Kim,M.F., Davydov,E., Green,E.D., Sidow,A. and Batzoglou,S. (2003) LAGAN and Multi-LAGAN: efficient tools for large-scale multiple alignment of genomic DNA. Genome Res., 13, 721731.
[Abstract/Free Full Text] - Hirosawa,M., Totoki,Y., Hoshida,M. and Ishikawa,M. (1995) Comprehensive study on iterative algorithms of multiple sequence alignment. CABIOS, 11, 1318.
- Thompson,J.D., Plewniak,F. and Poch,O. (1999a) BAliBASE: a benchmark alignment database for the evaluation of multiple alignment programs. Bioinformatics, 15, 8788.
[Abstract/Free Full Text] - Bahr,A., Thompson,J.D., Thierry,J.C. and Poch,O. (2001) BAliBASE (Benchmark Alignment dataBASE): enhancements for repeats, transmembrane sequences and circular permutations. Nucleic Acids Res., 29, 323326.
[Abstract/Free Full Text] - Van Walle,I., Lasters,I. and Wyns,L. (2004) Align-ma new algorithm for multiple alignment of highly divergent sequences. Bioinformatics, DOI: 10.1093/bioinformatics/bth116.
- Schultz,J., Milpetz,F., Bork,P. and Ponting,C.P. (1998) SMART, a simple modular architecture research tool: identification of signaling domains. Proc. Natl Acad. Sci. USA, 95, 58575864.
[Abstract/Free Full Text] - Schultz,J., Copley,R.R., Doerks,T., Ponting,C.P. and Bork,P. (2000) SMART: a web-based tool for the study of genetically mobile domains. Nucleic Acids Res., 28, 231234.
[Abstract/Free Full Text] - Ponting,C.P., Schultz,J., Milpetz,F. and Bork,P. (1999) SMART: identification and annotation of domains from signalling and extracellular protein sequences. Nucleic Acids Res., 27, 229332.
[Abstract/Free Full Text] - Thompson,J.D., Higgins,D.G. and Gibson,T.J. (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res., 22, 46734680.
[Abstract/Free Full Text] - Brenner,S.E., Koehl,P. and Levitt,M. (2000) The ASTRAL compendium for protein structure and sequence analysis. Nucleic Acids Res., 28, 254256.
[Abstract/Free Full Text] - Murzin,A.G., Brenner,S.E., Hubbard,T. and Chothia,C. (1995) SCOP: a structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol., 247, 536540.[CrossRef][Web of Science][Medline]
- Boutonnet,N.S., Rooman,M.J., Ochagavia,M.E., Richelle,J. and Wodak,S.J. (1995) Optimal protein structure alignments by multiple linkage clustering: application to distantly related proteins. Protein Eng., 8, 647662.[Web of Science][Medline]
- Shindyalov,I.N. and Bourne,P.E. (1998) Protein structure alignment by incremental combinatorial extension (CE) of the optimal path. Protein Eng., 11, 739747.
[Abstract/Free Full Text] - Fischer,D., Barret,C., Bryson,K., Elofsson,A., Godzik,A., Jones,D., Karplus,K.J., Kelley,L.A., MacCallum,R.M., Pawowski,K., Rost,B., Rychlewski,L. and Sternberg,M. (1999) CAFASP-1: critical assessment of fully automated structure prediction methods. Proteins, Suppl. 3, 209217.
- Eidhammer,I., Jonassen,I. and Taylor,W.R. (2000) Structure comparison and structure patterns. J. Comput. Biol., 7, 685716.[CrossRef][Web of Science][Medline]
- Sauder,J.M., Arthur,J.W. and Dunbrack,R.L.,Jr (2000) Large-scale comparison of protein sequence alignment algorithms with structure alignments. Proteins, 40, 622.[CrossRef][Web of Science][Medline]
- Leplae,R. and Hubbard,T.J. (2002) MaxBench: evaluation of sequence and structure comparison methods. Bioinformatics, 18, 494495.
[Abstract/Free Full Text] - Sadreyev,R. and Grishin,N. (2003) COMPASS: a tool for comparison of multiple protein alignments with assessment of statistical significance. J. Mol. Biol., 326, 317336.[CrossRef][Web of Science][Medline]
- Edgar,R.C. and Sjolander,K. (2004) COACH: profile-profile alignment of protein families using hidden Markov models. Bioinformatics, DOI: 10.1093/bioinformatics/bth091.
- Holm,L. and Sander,C. (1998) Touring protein fold space with Dali/FSSP. Nucleic Acids Res., 26, 316319.
[Abstract/Free Full Text] - Altschul,S.F., Madden,T.L., Schaffer,A.A., Zhang,J., Zhang,Z., Miller,W. and Lipman,D.J. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res., 25, 33893402.
[Abstract/Free Full Text] - Schaffer,A.A., Aravind,L., Madden,T.L., Shavirin,S., Spouge,J.L., Wolf,Y.I., Koonin,E.V. and Altschul,S.F. (2001) Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements. Nucleic Acids Res., 29, 29943005.
[Abstract/Free Full Text] - Pruitt,K.D., Tatusova,T. and Maglott,D.R. (2003) NCBI Reference Sequence project: update and current status. Nucleic Acids Res., 31, 3437.
[Abstract/Free Full Text] - Thompson,J.D., Plewniak,F. and Poch,O. (1999b) A comprehensive comparison of multiple sequence alignment programs. Nucleic Acids Res., 27, 26822690.
[Abstract/Free Full Text] - OSullivan,O., Zehnder,M., Higgins,D., Bucher,P., Grosdidier,A. and Notredame,C. (2003) APDB: a novel measure for benchmarking sequence alignment methods without reference alignments. Bioinformatics, 19 Suppl. 1, I215I221.
- Friedman,M. (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc., 32, 675701.[CrossRef][Web of Science]
- Stoye,J., Evers,D. and Meyer,F. (1998) Rose: generating sequence families. Bioinformatics, 14, 157163.
[Abstract/Free Full Text] - Sjolander,K. (1998) Phylogenetic inference in protein superfamilies: analysis of SH2 domains. Proc. Int. Conf. Intell. Syst. Mol. Biol., 6, 165174.[Medline]
This article has been cited by other articles:
![]() |
J. Zielonka, I. G. Bravo, D. Marino, E. Conrad, M. Perkovic, M. Battenberg, K. Cichutek, and C. Munk Restriction of Equine Infectious Anemia Virus by Equine APOBEC3 Cytidine Deaminases J. Virol., August 1, 2009; 83(15): 7547 - 7559. [Abstract] [Full Text] [PDF] |
||||
![]() |
C.-H. Kuo and H. Ochman Deletional Bias across the Three Domains of Life Gen Biol Evol, July 10, 2009; 2009(0): 145 - 152. [Abstract] [Full Text] [PDF] |
||||
![]() |
E. Heytens, J. Parrington, K. Coward, C. Young, S. Lambrecht, S.-Y. Yoon, R.A. Fissore, R. Hamer, C.M. Deane, M. Ruas, et al. Reduced amounts and abnormal forms of phospholipase C zeta (PLC{zeta}) in spermatozoa from infertile men Hum. Reprod., July 10, 2009; (2009) dep207v2. [Abstract] [Full Text] [PDF] |
||||
![]() |
M. Savic, J. Lovric, T. I. Tomic, B. Vasiljevic, and G. L. Conn Determination of the target nucleosides for members of two families of 16S rRNA methyltransferases that confer resistance to partially overlapping groups of aminoglycoside antibiotics Nucleic Acids Res., July 9, 2009; (2009) gkp575v1. [Abstract] [Full Text] [PDF] |
||||
![]() |
E. Kazancioglu, T. J. Near, R. Hanel, and P. C. Wainwright Influence of sexual selection and feeding functional morphology on diversification rate of parrotfishes (Scaridae) Proc R Soc B, July 8, 2009; (2009) rspb.2009.0876v1. [Abstract] [Full Text] [PDF] |
||||
![]() |
M. Balke, I. Ribera, L. Hendrich, M. A. Miller, K. Sagata, A. Posman, A. P. Vogler, and R. Meier New Guinea highland origin of a widespread arthropod supertramp Proc R Soc B, July 7, 2009; 276(1666): 2359 - 2367. [Abstract] [Full Text] [PDF] |
||||
![]() |
J. Martin-Coello, H. Dopazo, L. Arbiza, J. Ausio, E. R.S. Roldan, and M. Gomendio Sexual selection drives weak positive selection in protamine genes and high promoter divergence, enhancing sperm competitiveness Proc R Soc B, July 7, 2009; 276(1666): 2427 - 2436. [Abstract] [Full Text] [PDF] |
||||
![]() |
M. E. Siddall, F. M. Fontanella, S. C. Watson, S. Kvist, and C. Erseus Barcoding Bamboozled by Bacteria: Convergence to Metazoan Mitochondrial Primer Targets by Marine Microbes Syst Biol, July 6, 2009; (2009) syp033v1. [Full Text] [PDF] |
||||
![]() |
J. Oberto, N. Breuil, A. Hecker, F. Farina, C. Brochier-Armanet, E. Culetto, and P. Forterre Qri7/OSGEPL, the mitochondrial version of the universal Kae1/YgjD protein, is essential for mitochondrial genome maintenance Nucleic Acids Res., July 3, 2009; (2009) gkp557v1. [Abstract] [Full Text] [PDF] |
||||
![]() |
S. Rivera, J. F.X. Wellehan Jr, R. McManamon, C. J. Innis, M. M. Garner, B. L. Raphael, C. R. Gregory, K. S. Latimer, C. E. Rodriguez, O. Diaz-Figueroa, et al. Systemic adenovirus infection in Sulawesi tortoises (Indotestudo forsteni) caused by a novel siadenovirus J Vet Diagn Invest, July 1, 2009; 21(4): 415 - 426. [Abstract] [Full Text] [PDF] |
||||
![]() |
J. A. Galeota, J. E. Napier, D. L. Armstrong, J.-J. Riethoven, and D. G. Rogers Herpesvirus infections in rock hyraxes (Procavia capensis) J Vet Diagn Invest, July 1, 2009; 21(4): 531 - 535. [Abstract] [Full Text] [PDF] |
||||
![]() |
A. Kumar and L. Cowen Augmented training of hidden Markov models to recognize remote homologs via simulated evolution Bioinformatics, July 1, 2009; 25(13): 1602 - 1608. [Abstract] [Full Text] [PDF] |
||||
![]() |
B. Contreras-Moreira, B. Sachman-Ruiz, I. Figueroa-Palacios, and P. Vinuesa primers4clades: a web server that uses phylogenetic trees to design lineage-specific PCR primers for metagenomic and diversity studies Nucleic Acids Res., July 1, 2009; 37(suppl_2): W95 - W100. [Abstract] [Full Text] [PDF] |
||||
![]() |
M. D. Greif, A. M. Stchigel, A. N. Miller, and S. M. Huhndorf A re-evaluation of genus Chaetomidium based on molecular and morphological characters Mycologia, July 1, 2009; 101(4): 554 - 564. [Abstract] [Full Text] [PDF] |
||||
![]() |
W. T. M. Mooij, E. Mitsiki, and A. Perrakis ProteinCCD: enabling the design of protein truncation constructs for expression and crystallization experiments Nucleic Acids Res., July 1, 2009; 37(suppl_2): W402 - W405. [Abstract] [Full Text] [PDF] |
||||
![]() |
S. Sankararaman, B. Kolaczkowski, and K. Sjolander INTREPID: a web server for prediction of functionally important residues by evolutionary analysis Nucleic Acids Res., July 1, 2009; 37(suppl_2): W390 - W395. [Abstract] [Full Text] [PDF] |
||||
![]() |
J.-L. Pons and G. Labesse @TOME-2: a new pipeline for comparative modeling of protein-ligand complexes Nucleic Acids Res., July 1, 2009; 37(suppl_2): W485 - W491. [Abstract] [Full Text] [PDF] |
||||
![]() |
F. Schreiber, G. Worheide, and B. Morgenstern OrthoSelect: a web server for selecting orthologous gene alignments from EST sequences Nucleic Acids Res., July 1, 2009; 37(suppl_2): W185 - W188. [Abstract] [Full Text] [PDF] |
||||
![]() |
J. C. Opazo, A. M. Sloan, K. L. Campbell, and J. F. Storz Origin and Ascendancy of a Chimeric Fusion Gene: The {beta}/{delta}-Globin Gene of Paenungulate Mammals Mol. Biol. Evol., July 1, 2009; 26(7): 1469 - 1478. [Abstract] [Full Text] [PDF] |
||||
![]() |
A. Atteia, A. Adrait, S. Brugiere, M. Tardif, R. van Lis, O. Deusch, T. Dagan, L. Kuhn, B. Gontero, W. Martin, et al. A Proteomic Survey of Chlamydomonas reinhardtii Mitochondria Sheds New Light on the Metabolic Plasticity of the Organelle and on the Nature of the {alpha}-Proteobacterial Mitochondrial Ancestor Mol. Biol. Evol., July 1, 2009; 26(7): 1533 - 1548. [Abstract] [Full Text] [PDF] |
||||
![]() |
T. Zhou, M. Weems, and C. O. Wilke Translationally Optimal Codons Associate with Structurally Sensitive Sites in Proteins Mol. Biol. Evol., July 1, 2009; 26(7): 1571 - 1580. [Abstract] [Full Text] [PDF] |
||||
![]() |
M. Sandoval-Calderon, O. Geiger, Z. Guan, F. Barona-Gomez, and C. Sohlenkamp A Eukaryote-like Cardiolipin Synthase Is Present in Streptomyces coelicolor and in Most Actinobacteria J. Biol. Chem., June 26, 2009; 284(26): 17383 - 17390. [Abstract] [Full Text] [PDF] |
||||
![]() |
V. E. Mayer and H. Voglmayr Mycelial carton galleries of Azteca brevis (Formicidae) as a multi-species network Proc R Soc B, June 25, 2009; (2009) rspb.2009.0768v1. [Abstract] [Full Text] [PDF] |
||||
![]() |
C. J. Pink, S. K. Swaminathan, I. Dunham, J. Rogers, A. Ward, and L. D. Hurst Evidence That Replication-Associated Mutation Alone Does Not Explain Between-Chromosome Differences In Substitution Rates Gen Biol Evol, June 22, 2009; 2009(0): 13 - 22. [Abstract] [Full Text] [PDF] |
||||
![]() |
C.-c. Huang, K. Yoshino-Koh, and J. J. G. Tesmer A Surface of the Kinase Domain Critical for the Allosteric Activation of G Protein-coupled Receptor Kinases J. Biol. Chem., June 19, 2009; 284(25): 17206 - 17215. [Abstract] [Full Text] [PDF] |
||||
![]() |
S. D. Liberles, L. F. Horowitz, D. Kuang, J. J. Contos, K. L. Wilson, J. Siltberg-Liberles, D. A. Liberles, and L. B. Buck Formyl peptide receptors are candidate chemosensory receptors in the vomeronasal organ PNAS, June 16, 2009; 106(24): 9842 - 9847. [Abstract] [Full Text] [PDF] |
||||
![]() |
J. Ramana and D. Gupta ProtVirDB: a database of protozoan virulent proteins Bioinformatics, June 15, 2009; 25(12): 1568 - 1569. [Abstract] [Full Text] [PDF] |
||||
![]() |
H. Spahr, G. Calero, D. A. Bushnell, and R. D. Kornberg Schizosacharomyces pombe RNA polymerase II at 3.6-A resolution PNAS, June 9, 2009; 106(23): 9185 - 9190. [Abstract] [Full Text] [PDF] |
||||
![]() |
K. M. Hamilton, P. W. Shaw, and D. Morritt Prevalence and seasonality of Hematodinium (Alveolata: Syndinea) in a Scottish crustacean community ICES J. Mar. Sci., June 8, 2009; (2009) fsp152v1. [Abstract] [Full Text] [PDF] |
||||
![]() |
Z. Wunderlich and L. A. Mirny Using genome-wide measurements for computational prediction of SH2-peptide interactions Nucleic Acids Res., June 5, 2009; (2009) gkp394v1. [Abstract] [Full Text] [PDF] |
||||
![]() |
B. Jacobs, F. Lens, and E. Smets Evolution of fruit and seed characters in the Diervilla and Lonicera clades (Caprifoliaceae, Dipsacales) Ann. Bot., June 5, 2009; (2009) mcp131v1. [Abstract] [Full Text] [PDF] |
||||
![]() |
P. H. Degnan, Y. Yu, N. Sisneros, R. A. Wing, and N. A. Moran Hamiltonella defensa, genome evolution of protective bacterial endosymbiont from pathogenic ancestors PNAS, June 2, 2009; 106(22): 9063 - 9068. [Abstract] [Full Text] [PDF] |
||||
![]() |
S. Duffy and E. C. Holmes Validation of high rates of nucleotide substitution in geminiviruses: phylogenetic evidence from East African cassava mosaic viruses J. Gen. Virol., June 1, 2009; 90(6): 1539 - 1547. [Abstract] [Full Text] [PDF] |
||||
![]() |
D. A. Antonopoulos, S. M. Huse, H. G. Morrison, T. M. Schmidt, M. L. Sogin, and V. B. Young Reproducible Community Dynamics of the Gastrointestinal Microbiota following Antibiotic Perturbation Infect. Immun., June 1, 2009; 77(6): 2367 - 2375. [Abstract] [Full Text] [PDF] |
||||
![]() |
Y. Sun, Y. Cai, L. Liu, F. Yu, M. L. Farrell, W. McKendree, and W. Farmerie ESPRIT: estimating species richness using large collections of 16S rRNA pyrosequences Nucleic Acids Res., June 1, 2009; 37(10): e76 - e76. [Abstract] [Full Text] [PDF] |
||||
![]() |
Y. Han, J. M. Burnette III, and S. R. Wessler TARGeT: a web-based pipeline for retrieving and characterizing gene and transposable element families from genomic sequences Nucleic Acids Res., June 1, 2009; 37(11): e78 - e78. [Abstract] [Full Text] [PDF] |
||||
![]() |
D. D. Nedialkova, R. Ulferts, E. van den Born, C. Lauber, A. E. Gorbalenya, J. Ziebuhr, and E. J. Snijder Biochemical Characterization of Arterivirus Nonstructural Protein 11 Reveals the Nidovirus-Wide Conservation of a Replicative Endoribonuclease J. Virol., June 1, 2009; 83(11): 5671 - 5682. [Abstract] [Full Text] [PDF] |
||||
![]() |
A. R. Wattam, K. P. Williams, E. E. Snyder, N. F. Almeida Jr., M. Shukla, A. W. Dickerman, O. R. Crasta, R. Kenyon, J. Lu, J. M. Shallom, et al. Analysis of Ten Brucella Genomes Reveals Evidence for Horizontal Gene Transfer Despite a Preferred Intracellular Lifestyle J. Bacteriol., June 1, 2009; 191(11): 3569 - 3579. [Abstract] [Full Text] [PDF] |
||||
![]() |
K. F. Ettwig, T. van Alen, K. T. van de Pas-Schoonen, M. S. M. Jetten, and M. Strous Enrichment and Molecular Detection of Denitrifying Methanotrophic Bacteria of the NC10 Phylum Appl. Envir. Microbiol., June 1, 2009; 75(11): 3656 - 3662. [Abstract] [Full Text] [PDF] |
||||
![]() |
A. Marchais, M. Naville, C. Bohn, P. Bouloc, and D. Gautheret Single-pass classification of all noncoding sequences in a bacterial genome using phylogenetic profiles Genome Res., June 1, 2009; 19(6): 1084 - 1092. [Abstract] [Full Text] [PDF] |
||||
![]() |
F.-C. Chen, Y.-Z. Chen, and T.-J. Chuang CNVVdb: a database of copy number variations across vertebrate genomes Bioinformatics, June 1, 2009; 25(11): 1419 - 1421. [Abstract] [Full Text] [PDF] |
||||
![]() |
M. Samuels, G. Gulati, J.-H. Shin, R. Opara, E. McSweeney, M. Sekedat, S. Long, Z. Kelman, and D. Jeruzalmi A biochemically active MCM-like helicase in Bacillus cereus Nucleic Acids Res., May 27, 2009; (2009) gkp376v1. [Abstract] [Full Text] [PDF] |
||||
![]() |
T. J. Hamp, W. J. Jones, and A. A. Fodor Effects of Experimental Choices and Analysis Noise on Surveys of the "Rare Biosphere" Appl. Envir. Microbiol., May 15, 2009; 75(10): 3263 - 3270. [Abstract] [Full Text] [PDF] |
||||
![]() |
Y. I. Wolf, P. S. Novichkov, G. P. Karev, E. V. Koonin, and D. J. Lipman Inaugural Article: The universal distribution of evolutionary rates of genes and distinct characteristics of eukaryotic genes of different apparent ages PNAS, May 5, 2009; 106(18): 7273 - 7280. [Abstract] [Full Text] [PDF] |
||||
![]() |
L. Childs, Z. Nikoloski, P. May, and D. Walther Identification and classification of ncRNA molecules using graph properties Nucleic Acids Res., May 1, 2009; 37(9): e66 - e66. [Abstract] [Full Text] [PDF] |
||||
![]() |
D. P. R. Herlemann, O. Geissinger, W. Ikeda-Ohtsubo, V. Kunin, H. Sun, A. Lapidus, P. Hugenholtz, and A. Brune Genomic Analysis of "Elusimicrobium minutum," the First Cultivated Representative of the Phylum "Elusimicrobia" (Formerly Termite Group 1) Appl. Envir. Microbiol., May 1, 2009; 75(9): 2841 - 2849. [Abstract] [Full Text] [PDF] |
||||
![]() |
A. M. Waterhouse, J. B. Procter, D. M. A. Martin, M. Clamp, and G. J. Barton Jalview Version 2--a multiple sequence alignment editor and analysis workbench Bioinformatics, May 1, 2009; 25(9): 1189 - 1191. [Abstract] [Full Text] [PDF] |
||||
![]() |
J. F. Petrosino, S. Highlander, R. A. Luna, R. A. Gibbs, and J. Versalovic Metagenomic Pyrosequencing and Microbial Identification Clin. Chem., May 1, 2009; 55(5): 856 - 866. [Abstract] [Full Text] [PDF] |
||||
![]() |
T. Rausch, S. Koren, G. Denisov, D. Weese, A.-K. Emde, A. Doring, and K. Reinert A consistency-based consensus algorithm for de novo and reference-guided sequence assembly of short reads Bioinformatics, May 1, 2009; 25(9): 1118 - 1124. [Abstract] [Full Text] [PDF] |
||||
![]() |
J. da Silva Amino Acid Covariation in a Functionally Important Human Immunodeficiency Virus Type 1 Protein Region Is Associated With Population Subdivision Genetics, May 1, 2009; 182(1): 265 - 275. [Abstract] [Full Text] [PDF] |
||||
![]() |
N. Lewis-Rogers, M. L. Bendall, and K. A. Crandall Phylogenetic Relationships and Molecular Adaptation Dynamics of Human Rhinoviruses Mol. Biol. Evol., May 1, 2009; 26(5): 969 - 981. [Abstract] [Full Text] [PDF] |
||||
![]() |
H. Ramsay, L. H. Rieseberg, and K. Ritland The Correlation of Evolutionary Rate with Pathway Position in Plant Terpenoid Biosynthesis Mol. Biol. Evol., May 1, 2009; 26(5): 1045 - 1053. [Abstract] [Full Text] [PDF] |
||||
![]() |
S. Kelm, J. Shi, and C. M. Deane iMembrane: homology-based membrane-insertion of proteins Bioinformatics, April 15, 2009; 25(8): 1086 - 1088. [Abstract] [Full Text] [PDF] |
||||
![]() |
M. Timmins, S. R. Thomas-Hall, A. Darling, E. Zhang, B. Hankamer, U. C. Marx, and P. M. Schenk Phylogenetic and molecular analysis of hydrogen-producing green algae J. Exp. Bot., April 2, 2009; (2009) erp052v1. [Abstract] [Full Text] [PDF] |
||||
![]() |
S. Smit, R. Knight, and J. Heringa RNA structure prediction from evolutionary patterns of nucleotide composition Nucleic Acids Res., April 1, 2009; 37(5): 1378 - 1386. [Abstract] [Full Text] [PDF] |
||||
![]() |
N. L. Ward, J. F. Challacombe, P. H. Janssen, B. Henrissat, P. M. Coutinho, M. Wu, G. Xie, D. H. Haft, M. Sait, J. Badger, et al. Three Genomes from the Phylum Acidobacteria Provide Insight into the Lifestyles of These Microorganisms in Soils Appl. Envir. Microbiol., April 1, 2009; 75(7): 2046 - 2056. [Abstract] [Full Text] [PDF] |
||||
![]() |
T. Hou, Z. Xu, W. Zhang, W. A. McLaughlin, D. A. Case, Y. Xu, and W. Wang Characterization of Domain-Peptide Interaction Interface: A Generic Structure-based Model to Decipher the Binding Specificity of SH3 Domains Mol. Cell. Proteomics, April 1, 2009; 8(4): 639 - 649. [Abstract] [Full Text] [PDF] |
||||
![]() |
S. Banerjee, P. W Robbins, and J. Samuelson Molecular characterization of nucleocytosolic O-GlcNAc transferases of Giardia lamblia and Cryptosporidium parvum Glycobiology, April 1, 2009; 19(4): 331 - 336. [Abstract] [Full Text] [PDF] |
||||
![]() |
S. A. James, M. J.T. O'Kelly, D. M. Carter, R. P. Davey, A. van Oudenaarden, and I. N. Roberts Repetitive sequence variation and dynamics in the ribosomal DNA array of Saccharomyces cerevisiae as revealed by whole-genome resequencing Genome Res., April 1, 2009; 19(4): 626 - 635. [Abstract] [Full Text] [PDF] |
||||
![]() |
J. E. G. Gallagher, J. E. Babiarz, L. Teytelman, K. H. Wolfe, and J. Rine Elaboration, Diversification and Regulation of the Sir1 Family of Silencing Proteins in Saccharomyces Genetics, April 1, 2009; 181(4): 1477 - 1491. [Abstract] [Full Text] [PDF] |
||||
![]() |
T. Miyake, N. Takebayashi, and D. E. Wolf Possible Diversifying Selection in the Imprinted Gene, MEDEA, in Arabidopsis Mol. Biol. Evol., April 1, 2009; 26(4): 843 - 857. [Abstract] [Full Text] [PDF] |
||||
![]() |
J. Stuttmann, E. Lechner, R. Guerois, J. E. Parker, L. Nussaume, P. Genschik, and L. D. Noel COP9 Signalosome- and 26S Proteasome-dependent Regulation of SCFTIR1 Accumulation in Arabidopsis J. Biol. Chem., March 20, 2009; 284(12): 7920 - 7930. [Abstract] [Full Text] [PDF] |
||||
![]() |
L. G. Pell, V. Kanelis, L. W. Donaldson, P. Lynne Howell, and A. R. Davidson The phage {lambda} major tail protein structure reveals a common evolution for long-tailed phages and the type VI bacterial secretion system PNAS, March 17, 2009; 106(11): 4160 - 4165. [Abstract] [Full Text] [PDF] |
||||
![]() |
S. M. Coyle, W. V. Gilbert, and J. A. Doudna Direct Link between RACK1 Function and Localization at the Ribosome In Vivo Mol. Cell. Biol., March 15, 2009; 29(6): 1626 - 1634. [Abstract] [Full Text] [PDF] |
||||
![]() |
Y. Nakajima, R. G. Tyers, C. C.L. Wong, J. R. Yates III, D. G. Drubin, and G. Barnes Nbl1p: A Borealin/Dasra/CSC-1-like Protein Essential for Aurora/Ipl1 Complex Function and Integrity in Saccharomyces cerevisiae Mol. Biol. Cell, March 15, 2009; 20(6): 1772 - 1784. [Abstract] [Full Text] [PDF] |
||||
![]() |
M. J Raupach, C. Mayer, M. Malyutina, and J.-W. Wagele Multiple origins of deep-sea Asellota (Crustacea: Isopoda) from shallow waters revealed by molecular data Proc R Soc B, March 7, 2009; 276(1658): 799 - 808. [Abstract] [Full Text] [PDF] |
||||
![]() |
B. Chagot, F. Potet, J. R. Balser, and W. J. Chazin Solution NMR Structure of the C-terminal EF-hand Domain of Human Cardiac Sodium Channel NaV1.5 J. Biol. Chem., March 6, 2009; 284(10): 6436 - 6445. [Abstract] [Full Text] [PDF] |
||||
![]() |
Y. Yan, A. Buckler-White, K. Wollenberg, and C. A. Kozak Origin, antiviral function and evidence for positive selection of the gammaretrovirus restriction gene Fv1 in the genus Mus PNAS, March 3, 2009; 106(9): 3259 - 3263. [Abstract] [Full Text] [PDF] |
||||
![]() |
D. Libkind, M. Gadanho, M. van Broock, and J. P. Sampaio Cystofilobasidium lacus-mascardii sp. nov., a basidiomycetous yeast species isolated from aquatic environments of the Patagonian Andes, and Cystofilobasidium macerans sp. nov., the sexual stage of Cryptococcus macerans Int J Syst Evol Microbiol, March 1, 2009; 59(3): 622 - 630. [Abstract] [Full Text] [PDF] |
||||
![]() |
W. C. Wilson, E. S. O'Hearn, C. Tellgren-Roth, D. E. Stallknecht, D. G. Mead, and J. O. Mecham Detection of all eight serotypes of Epizootic hemorrhagic disease virus by real-time reverse transcription polymerase chain reaction J Vet Diagn Invest, March 1, 2009; 21(2): 220 - 225. [Abstract] [Full Text] [PDF] |
||||
![]() |
M. Romeralo, S.L. Baldauf, and J.C. Cavender A new species of cellular slime mold from southern Portugal based on morphology, ITS and SSU sequences Mycologia, March 1, 2009; 101(2): 269 - 274. [Abstract] [Full Text] [PDF] |
||||
![]() |
M. Turmel, M.-C. Gagnon, C. J. O'Kelly, C. Otis, and C. Lemieux The Chloroplast Genomes of the Green Algae Pyramimonas, Monomastix, and Pycnococcus Shed New light on the Evolutionary History of Prasinophytes and the Origin of the Secondary Chloroplasts of Euglenids Mol. Biol. Evol., March 1, 2009; 26(3): 631 - 648. [Abstract] [Full Text] [PDF] |
||||
![]() |
H. Liu, Y. Fu, D. Jiang, G. Li, J. Xie, Y. Peng, X. Yi, and S. A. Ghabrial A Novel Mycovirus That Is Related to the Human Pathogen Hepatitis E Virus and Rubi-Like Viruses J. Virol., February 15, 2009; 83(4): 1981 - 1991. [Abstract] [Full Text] [PDF] |
||||
![]() |
D. Linde, I. Macias, L. Fernandez-Arrojo, F. J. Plou, A. Jimenez, and M. Fernandez-Lobato Molecular and Biochemical Characterization of a {beta}-Fructofuranosidase from Xanthophyllomyces dendrorhous Appl. Envir. Microbiol., February 15, 2009; 75(4): 1065 - 1073. [Abstract] [Full Text] [PDF] |
||||
![]() |
N. E. Davey, D. C. Shields, and R. J. Edwards Masking residues using context-specific evolutionary conservation significantly improves short linear motif discovery Bioinformatics, February 15, 2009; 25(4): 443 - 450. [Abstract] [Full Text] [PDF] |
||||
![]() |
Y. Yin, F. Bangs, I. R. Paton, A. Prescott, J. James, M. G. Davey, P. Whitley, G. Genikhovich, U. Technau, D. W. Burt, et al. The Talpid3 gene (KIAA0586) encodes a centrosomal protein that is essential for primary cilia formation Development, February 15, 2009; 136(4): 655 - 664. [Abstract] [Full Text] [PDF] |
||||
![]() |
J.M. ten Cate, F.M. Klis, T. Pereira-Cenci, W. Crielaard, and P.W.J. de Groot Molecular and Cellular Mechanisms That Lead to Candida Biofilm Formation Journal of Dental Research, February 1, 2009; 88(2): 105 - 115. [Abstract] [Full Text] [PDF] |
||||
![]() |
S. E. Morales, T. F. Cosart, J. V. Johnson, and W. E. Holben Extensive Phylogenetic Analysis of a Soil Bacterial Community Illustrates Extreme Taxon Evenness and the Effects of Amplicon Length, Degree of Coverage, and DNA Fractionation on Classification and Ecological Parameters Appl. Envir. Microbiol., February 1, 2009; 75(3): 668 - 675. [Abstract] [Full Text] [PDF] |
||||
![]() |
C. Dherin, E. Gueneau, M. Francin, M. Nunez, S. Miron, S. E. Liberti, L. J. Rasmussen, S. Zinn-Justin, B. Gilquin, J.-B. Charbonnier, et al. Characterization of a Highly Conserved Binding Site of Mlh1 Required for Exonuclease I-Dependent Mismatch Repair Mol. Cell. Biol., February 1, 2009; 29(3): 907 - 918. [Abstract] [Full Text] [PDF] |
||||
![]() |
P. A. Beare, N. Unsworth, M. Andoh, D. E. Voth, A. Omsland, S. D. Gilk, K. P. Williams, B. W. Sobral, J. J. Kupko III, S. F. Porcella, et al. Comparative Genomics Reveal Extensive Transposon-Mediated Genomic Plasticity and Diversity among Potential Effector Proteins within the Genus Coxiella Infect. Immun., February 1, 2009; 77(2): 642 - 656. [Abstract] [Full Text] [PDF] |
||||
![]() |
V. Sorokin, K. Severinov, and M. S. Gelfand Systematic prediction of control proteins and their DNA binding sites Nucleic Acids Res., February 1, 2009; 37(2): 441 - 451. [Abstract] [Full Text] [PDF] |
||||
![]() |
Y. Lu and S.-H. Sze Improving accuracy of multiple sequence alignment algorithms based on alignment of neighboring residues Nucleic Acids Res., February 1, 2009; 37(2): 463 - 472. [Abstract] [Full Text] [PDF] |
||||
![]() |
B. L. Schulz and M. Aebi Analysis of Glycosylation Site Occupancy Reveals a Role for Ost3p and Ost6p in Site-specific N-Glycosylation Efficiency Mol. Cell. Proteomics, February 1, 2009; 8(2): 357 - 364. [Abstract] [Full Text] [PDF] |
||||
![]() |
C. Brochier-Armanet, E. Talla, and S. Gribaldo The Multiple Evolutionary Histories of Dioxygen Reductases: Implications for the Origin and Evolution of Aerobic Respiration Mol. Biol. Evol., February 1, 2009; 26(2): 285 - 297. [Abstract] [Full Text] [PDF] |
||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||






























