Search

Abstract Background Profile hidden Markov models (profile-HMMs) are sensitive tools for remote protein homology detection, but the main scoring algorithms, Viterbi or Forward, require considerable time to search large sequence databases. Results We have designed a series of database filtering steps, HMMERHEAD, that are applied prior to the scoring algorithms, as implemented in the HMMER package, in an effort to reduce search time. Using this heuristic, we obtain a 20-fold decrease in Forward and a 6-fold decrease in Viterbi search time with a minimal loss in sensitivity relative to the unfiltered approaches. We then implemented an iterative profile-HMM search method, JackHMMER, which employs the HMMERHEAD heuristic. Due to our search heuristic, we eliminated the subdatabase creation that is common in current iterative profile-HMM approaches. On our benchmark, JackHMMER detects 14% more remote protein homologs than SAM's iterative method T2K. Conclusions Our search heuristic, HMMERHEAD, significantly reduces the time needed to score a profile-HMM against large sequence databases. This search heuristic allowed us to implement an iterative profile-HMM search method, JackHMMER, which detects significantly more remote protein homologs than SAM's T2K and NCBI's PSI-BLAST.

Abstract Background We are interested in the problem of predicting secondary structure for small sets of homologous RNAs, by incorporating limited comparative sequence information into an RNA folding model. The Sankoff algorithm for simultaneous RNA folding and alignment is a basis for approaches to this problem. There are two open problems in applying a Sankoff algorithm: development of a good unified scoring system for alignment and folding and development of practical heuristics for dealing with the computational complexity of the algorithm. Results We use probabilistic models (pair stochastic context-free grammars, pairSCFGs) as a unifying framework for scoring pairwise alignment and folding. A constrained version of the pairSCFG structural alignment algorithm was developed which assumes knowledge of a few confidently aligned positions (pins). These pins are selected based on the posterior probabilities of a probabilistic pairwise sequence alignment. Conclusion Pairwise RNA structural alignment improves on structure prediction accuracy relative to single sequence folding. Constraining on alignment is a straightforward method of reducing the runtime and memory requirements of the algorithm. Five practical implementations of the pairwise Sankoff algorithm – this work (Consan), David Mathews' Dynalign, Ian Holmes' Stemloc, Ivo Hofacker's PMcomp, and Jan Gorodkin's FOLDALIGN – have comparable overall performance with different strengths and weaknesses.

Abstract Background RNA secondary structure prediction methods based on probabilistic modeling can be developed using stochastic context-free grammars (SCFGs). Such methods can readily combine different sources of information that can be expressed probabilistically, such as an evolutionary model of comparative RNA sequence analysis and a biophysical model of structure plausibility. However, the number of free parameters in an integrated model for consensus RNA structure prediction can become untenable if the underlying SCFG design is too complex. Thus a key question is, what small, simple SCFG designs perform best for RNA secondary structure prediction? Results Nine different small SCFGs were implemented to explore the tradeoffs between model complexity and prediction accuracy. Each model was tested for single sequence structure prediction accuracy on a benchmark set of RNA secondary structures. Conclusions Four SCFG designs had prediction accuracies near the performance of current energy minimization programs. One of these designs, introduced by Knudsen and Hein in their PFOLD algorithm, has only 21 free parameters and is significantly simpler than the others.

Abstract Background For many RNA molecules, secondary structure rather than primary sequence is the evolutionarily conserved feature. No programs have yet been published that allow searching a sequence database for homologs of a single RNA molecule on the basis of secondary structure. Results We have developed a program, RSEARCH, that takes a single RNA sequence with its secondary structure and utilizes a local alignment algorithm to search a database for homologous RNAs. For this purpose, we have developed a series of base pair and single nucleotide substitution matrices for RNA sequences called RIBOSUM matrices. RSEARCH reports the statistical confidence for each hit as well as the structural alignment of the hit. We show several examples in which RSEARCH outperforms the primary sequence search programs BLAST and SSEARCH. The primary drawback of the program is that it is slow. The C code for RSEARCH is freely available from our lab's website. Conclusion RSEARCH outperforms primary sequence programs in finding homologs of structured RNA sequences.

Abstract Background Covariance models (CMs) are probabilistic models of RNA secondary structure, analogous to profile hidden Markov models of linear sequence. The dynamic programming algorithm for aligning a CM to an RNA sequence of length N is O(N3) in memory. This is only practical for small RNAs. Results I describe a divide and conquer variant of the alignment algorithm that is analogous to memory-efficient Myers/Miller dynamic programming algorithms for linear sequence alignment. The new algorithm has an O(N2 log N) memory complexity, at the expense of a small constant factor in time. Conclusions Optimal ribosomal RNA structural alignments that previously required up to 150 GB of memory now require less than 270 MB.

Group I and Group II intron RNAs are both capable of self-splicing. However, while these two classes of introns follow similar self-splicing reactions, neither group possesses features that might indicate that they have a common evolutionary origin. Research that compares intermolecular structures of these two classes of self-splicing introns are described, with an emphasis on the link between amino acid sequences motifs and open reading frames.

Pharmacological Na+-glucose linked cotransporter (SGLT)2 inhibition is being examined as a renal protection strategy in nondiabetic chronic kidney disease. We quantified renal SGLT mRNA expression in healthy controls (HC), glomerulonephritis (GN), and diabetic kidney disease (DKD) to identify differences in expression across a spectrum of renal diseases. mRNA expression of SGLT1 and SGLT2 in renal tubules and glomeruli, obtained using microdissection and microarray techniques, was evaluated in two large cohorts. The European Renal cDNA bank included HC, GN, and DKD (98 glomeruli and 93 tubulointerstitium). The Nephrotic Syndrome Study Network cohort included 124 adults with membranous nephropathy, minimal change disease, focal segmental glomerulosclerosis, and IgA nephropathy. Within the European Renal cDNA bank, SGLT2 tubular and glomerular log2 mRNA expression significantly differed across HC, GN, and DKD (P = 0.0009 and P = 0.0004), with the highest expression in HC. Within the Nephrotic Syndrome Study Network, there were no differences in SGLT log2 mRNA expression across GN subtypes. Tubular SGLT2 log2 mRNA expression positively correlated with estimated glomerular filtration rate (by the Modification of Diet in Renal Disease Study equation) and glycated hemoglobin (r = 0.33 and 0.34, P < 0.05) and inversely correlated with interstitial fibrosis (r=-0.21, P < 0.05). In conclusion, SGLT2 mRNA expression was lower in DKD compared with HC or GN and inversely related to interstitial fibrosis. The relationships between SGLT mRNA, protein expression, and transporter activity require further elucidation. [ABSTRACT FROM AUTHOR]

Introduction: The current study aimed to investigate the association between urinary epidermal growth factor (uEGF) and renal disease severity and outcomes in patients with antineutrophil cytoplasmic antibody-associated vasculitis (AAV).Methods: Intrarenal EGFmRNA expression was extracted from transcriptomic data of microdissected tubulointerstitial compartments of kidney biopsies of patients with AAV. uEGF was measured in 173 patients with AAV in active stage and 143 in remission, and normalised to urine creatinine excretion (uEGF/Cr). The association between uEGF/Cr (or EGFmRNA) and clinical-pathological parameters was tested using linear regression analysis. The ability of uEGF/Cr to predict renal outcomes was analysed using Cox's regression analysis.Results: In patients with AAV, intrarenal EGFmRNA expression was significantly associated with estimated glomerular filtration rate (eGFR)(log2) at time of biopsy (β=0.63, p<0.001). The level of uEGF/Cr was significantly higher in patients in remission than in patients with active disease, both when looking at patients with sequential measurements (2.75±1.03vs 2.08±0.98, p<0.001) and in cross-sectional comparison. uEGF/Cr level was positively associated with eGFR(log2) at time of sampling in both active and remission stage (β=0.60, p<0.001; β=0.74, p<0.001, respectively). Patients with resistant renal disease had significantly lower uEGF/Cr levels than responders (1.65±1.22vs 2.16±1.26, p=0.04). Moreover, after adjusting for other potential predictors, uEGF/Cr was independently associated with composite endpoint of end-stage renal disease or 30% reduction of eGFR (HR 0.61, 95% CI 0.45 to 0.83, p=0.001).Conclusion: Lower uEGF/Cr levels are associated with more severe renal disease, renal resistance to treatment and higher risk of progression to composite outcome in patients with AAV. [ABSTRACT FROM AUTHOR]