DNA cytosine methyltransferases differentially regulate genome-wide hypermutation and interhomolog recombination in Trichoderma reesei meiosis.

Trichoderma reesei is an economically important enzyme producer with several unique meiotic features. spo11, the initiator of meiotic double-strand breaks (DSBs) in most sexual eukaryotes, is dispensable for T. reesei meiosis. T. reesei lacks the meiosis-specific recombinase Dmc1. Rad51 and Sae2, the activator of the Mre11 endonuclease complex, promote DSB repair and chromosome synapsis in wild-type and spo11Δ meiosis. DNA methyltransferases (DNMTs) perform multiple tasks in meiosis. Three DNMT genes (rid1, dim2 and dimX) differentially regulate genome-wide cytosine methylation and C:G-to-T:A hypermutations in different chromosomal regions. We have identified two types of DSBs: type I DSBs require spo11 or rid1 for initiation, whereas type II DSBs do not rely on spo11 and rid1 for initiation. rid1 (but not dim2) is essential for Rad51-mediated DSB repair and normal meiosis. rid1 and rad51 exhibit a locus heterogeneity (LH) relationship, in which LH-associated proteins often regulate interconnectivity in protein interaction networks. This LH relationship can be suppressed by deleting dim2 in a haploid rid1Δ (but not rad51Δ) parental strain, indicating that dim2 and rid1 share a redundant function that acts earlier than rad51 during early meiosis. In conclusion, our studies provide the first evidence of the involvement of DNMTs during meiotic initiation and recombination.

SpliceAPP: an interactive web server to predict splicing errors arising from human mutations.

BACKGROUND: Splicing variants are a major class of pathogenic mutations, with their severity equivalent to nonsense mutations. However, redundant and degenerate splicing signals hinder functional assessments of sequence variations within introns, particularly at branch sites. We have established a massively parallel splicing assay to assess the impact on splicing of 11,191 disease-relevant variants. Based on the experimental results, we then applied regression-based methods to identify factors determining splicing decisions and their respective weights. RESULTS: Our statistical modeling is highly sensitive, accurately annotating the splicing defects of near-exon intronic variants, outperforming state-of-the-art predictive tools. We have incorporated the algorithm and branchpoint information into a web-based tool, SpliceAPP, to provide an interactive application. This user-friendly website allows users to upload any genetic variants with genome coordinates (e.g., chr15 74,687,208 A G), and the tool will output predictions for splicing error scores and evaluate the impact on nearby splice sites. Additionally, users can query branch site information within the region of interest. CONCLUSIONS: In summary, SpliceAPP represents a pioneering approach to screening pathogenic intronic variants, contributing to the development of precision medicine. It also facilitates the annotation of splicing motifs. SpliceAPP is freely accessible using the link https://bc.imb.sinica.edu.tw/SpliceAPP . Source code can be downloaded at https://github.com/hsinnan75/SpliceAPP .

GSAlign: an efficient sequence alignment tool for intra-species genomes.

BACKGROUND: Personal genomics and comparative genomics are becoming more important in clinical practice and genome research. Both fields require sequence alignment to discover sequence conservation and variation. Though many methods have been developed, some are designed for small genome comparison while some are not efficient for large genome comparison. Moreover, most existing genome comparison tools have not been evaluated the correctness of sequence alignments systematically. A wrong sequence alignment would produce false sequence variants. RESULTS: In this study, we present GSAlign that handles large genome sequence alignment efficiently and identifies sequence variants from the alignment result. GSAlign is an efficient sequence alignment tool for intra-species genomes. It identifies sequence variations from the sequence alignments. We estimate performance by measuring the correctness of predicted sequence variations. The experiment results demonstrated that GSAlign is not only faster than most existing state-of-the-art methods, but also identifies sequence variants with high accuracy. CONCLUSIONS: As more genome sequences become available, the demand for genome comparison is increasing. Therefore an efficient and robust algorithm is most desirable. We believe GSAlign can be a useful tool. It exhibits the abilities of ultra-fast alignment as well as high accuracy and sensitivity for detecting sequence variations.

DART: a fast and accurate RNA-seq mapper with a partitioning strategy.

MOTIVATION: In recent years, the massively parallel cDNA sequencing (RNA-Seq) technologies have become a powerful tool to provide high resolution measurement of expression and high sensitivity in detecting low abundance transcripts. However, RNA-seq data requires a huge amount of computational efforts. The very fundamental and critical step is to align each sequence fragment against the reference genome. Various de novo spliced RNA aligners have been developed in recent years. Though these aligners can handle spliced alignment and detect splice junctions, some challenges still remain to be solved. With the advances in sequencing technologies and the ongoing collection of sequencing data in the ENCODE project, more efficient alignment algorithms are highly demanded. Most read mappers follow the conventional seed-and-extend strategy to deal with inexact matches for sequence alignment. However, the extension is much more time consuming than the seeding step. RESULTS: We proposed a novel RNA-seq de novo mapping algorithm, call DART, which adopts a partitioning strategy to avoid the extension step. The experiment results on synthetic datasets and real NGS datasets showed that DART is a highly efficient aligner that yields the highest or comparable sensitivity and accuracy compared to most state-of-the-art aligners, and more importantly, it spends the least amount of time among the selected aligners. AVAILABILITY AND IMPLEMENTATION: https://github.com/hsinnan75/DART. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Kart: a divide-and-conquer algorithm for NGS read alignment.

MOTIVATION: Next-generation sequencing (NGS) provides a great opportunity to investigate genome-wide variation at nucleotide resolution. Due to the huge amount of data, NGS applications require very fast and accurate alignment algorithms. Most existing algorithms for read mapping basically adopt seed-and-extend strategy, which is sequential in nature and takes much longer time on longer reads. RESULTS: We develop a divide-and-conquer algorithm, called Kart, which can process long reads as fast as short reads by dividing a read into small fragments that can be aligned independently. Our experiment result indicates that the average size of fragments requiring the more time-consuming gapped alignment is around 20 bp regardless of the original read length. Furthermore, it can tolerate much higher error rates. The experiments show that Kart spends much less time on longer reads than other aligners and still produce reliable alignments even when the error rate is as high as 15%. AVAILABILITY AND IMPLEMENTATION: Kart is available at https://github.com/hsinnan75/Kart/ . CONTACT: hsu@iis.sinica.edu.tw. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MAGIC-web: a platform for untargeted and targeted N-linked glycoprotein identification.

MAGIC-web is the first web server, to the best of our knowledge, that performs both untargeted and targeted analyses of mass spectrometry-based glycoproteomics data for site-specific N-linked glycoprotein identification. The first two modules, MAGIC and MAGIC+, are designed for untargeted and targeted analysis, respectively. MAGIC is implemented with our previously proposed novel Y1-ion pattern matching method, which adequately detects Y1- and Y0-ion without prior information of proteins and glycans, and then generates in silico MS(2) spectra that serve as input to a database search engine (e.g. Mascot) to search against a large-scale protein sequence database. On top of that, the newly implemented MAGIC+ allows users to determine glycopeptide sequences using their own protein sequence file. The third module, Reports Integrator, provides the service of combining protein identification results from Mascot and glycan-related information from MAGIC-web to generate a complete site-specific protein-glycan summary report. The last module, Glycan Search, is designed for the users who are interested in finding possible glycan structures with specific numbers and types of monosaccharides. The results from MAGIC, MAGIC+ and Reports Integrator can be downloaded via provided links whereas the annotated spectra and glycan structures can be visualized in the browser. MAGIC-web is accessible from http://ms.iis.sinica.edu.tw/MAGIC-web/index.html.

Mechanism and modeling of human disease-associated near-exon intronic variants that perturb RNA splicing.

It is estimated that 10%-30% of disease-associated genetic variants affect splicing. Splicing variants may generate deleteriously altered gene product and are potential therapeutic targets. However, systematic diagnosis or prediction of splicing variants is yet to be established, especially for the near-exon intronic splice region. The major challenge lies in the redundant and ill-defined branch sites and other splicing motifs therein. Here, we carried out unbiased massively parallel splicing assays on 5,307 disease-associated variants that overlapped with branch sites and collected 5,884 variants across the 5' splice region. We found that strong splice sites and exonic features preserve splicing from intronic sequence variation. Whereas the splice-altering mechanism of the 3' intronic variants is complex, that of the 5' is mainly splice-site destruction. Statistical learning combined with these molecular features allows precise prediction of altered splicing from an intronic variant. This statistical model provides the identity and ranking of biological features that determine splicing, which serves as transferable knowledge and out-performs the benchmarking predictive tool. Moreover, we demonstrated that intronic splicing variants may associate with disease risks in the human population. Our study elucidates the mechanism of splicing response of intronic variants, which classify disease-associated splicing variants for the promise of precision medicine.

The interactions between religion, religiosity, religious delusion/hallucination, and treatment-seeking behavior among schizophrenic patients in Taiwan.

Religion could influence the psychopathology, treatment-seeking behavior, and treatment outcome in schizophrenia, but the associations between these factors have never been explored thoroughly, and the data in Han-Chinese society are scarcer still. The current study recruited 55 schizophrenic patients to explore the relationship between religion, psychopathology with religious content, treatment-seeking behavior, and outcome. Subjects with religious delusions/hallucinations had lower scores on functioning and higher scores on religiosity. The higher religiosity scores were correlated with older age, longer duration of illness, religious affiliation, lower preference of psychiatric treatment, lower functioning score, and delusion/hallucination. As to treatment-seeking behavior, patients with religious affiliation showed less preference toward psychiatric treatment. Individuals with religious delusion/hallucination were more likely to receive magico-religious healing and not to be satisfied with psychiatric treatment. A more positive view of psychiatric treatment was predicted by lower religiosity score, higher satisfaction with psychiatric treatment, and lower years of education. The religiosity level seems not directly related to clinical severity, but it seems to be a better predictor of religious delusions/hallucinations than religious affiliation status. Patients with religious delusions/hallucinations did not necessarily have more severe psychopathology. There are different profiles associated with religious affiliation/religiosity and religious delusions/hallucinations in relation to treatment-seeking behavior among schizophrenia patients in Han-Chinese society.

Improving protein secondary structure prediction based on short subsequences with local structure similarity.

BACKGROUND: When characterizing the structural topology of proteins, protein secondary structure (PSS) plays an important role in analyzing and modeling protein structures because it represents the local conformation of amino acids into regular structures. Although PSS prediction has been studied for decades, the prediction accuracy reaches a bottleneck at around 80%, and further improvement is very difficult. RESULTS: In this paper, we present an improved dictionary-based PSS prediction method called SymPred, and a meta-predictor called SymPsiPred. We adopt the concept behind natural language processing techniques and propose synonymous words to capture local sequence similarities in a group of similar proteins. A synonymous word is an n-gram pattern of amino acids that reflects the sequence variation in a protein's evolution. We generate a protein-dependent synonymous dictionary from a set of protein sequences for PSS prediction.On a large non-redundant dataset of 8,297 protein chains (DsspNr-25), the average Q3 of SymPred and SymPsiPred are 81.0% and 83.9% respectively. On the two latest independent test sets (EVA Set_1 and EVA_Set2), the average Q3 of SymPred is 78.8% and 79.2% respectively. SymPred outperforms other existing methods by 1.4% to 5.4%. We study two factors that may affect the performance of SymPred and find that it is very sensitive to the number of proteins of both known and unknown structures. This finding implies that SymPred and SymPsiPred have the potential to achieve higher accuracy as the number of protein sequences in the NCBInr and PDB databases increases. CONCLUSIONS: Our experiment results show that local similarities in protein sequences typically exhibit conserved structures, which can be used to improve the accuracy of secondary structure prediction. For the application of synonymous words, we demonstrate an example of a sequence alignment which is generated by the distribution of shared synonymous words of a pair of protein sequences. We can align the two sequences nearly perfectly which are very dissimilar at the sequence level but very similar at the structural level. The SymPred and SymPsiPred prediction servers are available at http://bio-cluster.iis.sinica.edu.tw/SymPred/.

Protein subcellular localization prediction of eukaryotes using a knowledge-based approach.

BACKGROUND: The study of protein subcellular localization (PSL) is important for elucidating protein functions involved in various cellular processes. However, determining the localization sites of a protein through wet-lab experiments can be time-consuming and labor-intensive. Thus, computational approaches become highly desirable. Most of the PSL prediction systems are established for single-localized proteins. However, a significant number of eukaryotic proteins are known to be localized into multiple subcellular organelles. Many studies have shown that proteins may simultaneously locate or move between different cellular compartments and be involved in different biological processes with different roles. RESULTS: In this study, we propose a knowledge based method, called KnowPredsite, to predict the localization site(s) of both single-localized and multi-localized proteins. Based on the local similarity, we can identify the "related sequences" for prediction. We construct a knowledge base to record the possible sequence variations for protein sequences. When predicting the localization annotation of a query protein, we search against the knowledge base and used a scoring mechanism to determine the predicted sites. We downloaded the dataset from ngLOC, which consisted of ten distinct subcellular organelles from 1923 species, and performed ten-fold cross validation experiments to evaluate KnowPred site's performance. The experiment results show that KnowPred site achieves higher prediction accuracy than ngLOC and Blast-hit method. For single-localized proteins, the overall accuracy of KnowPred site is 91.7%. For multi-localized proteins, the overall accuracy of KnowPred site is 72.1%, which is significantly higher than that of ngLOC by 12.4%. Notably, half of the proteins in the dataset that cannot find any Blast hit sequence above a specified threshold can still be correctly predicted by KnowPred site. CONCLUSION: KnowPred site demonstrates the power of identifying related sequences in the knowledge base. The experiment results show that even though the sequence similarity is low, the local similarity is effective for prediction. Experiment results show that KnowPred site is a highly accurate prediction method for both single- and multi-localized proteins. It is worth-mentioning the prediction process of KnowPred site is transparent and biologically interpretable and it shows a set of template sequences to generate the prediction result. The KnowPred site prediction server is available at http://bio-cluster.iis.sinica.edu.tw/kbloc/.

N-GlyDE: a two-stage N-linked glycosylation site prediction incorporating gapped dipeptides and pattern-based encoding.

N-linked glycosylation is one of the predominant post-translational modifications involved in a number of biological functions. Since experimental characterization of glycosites is challenging, glycosite prediction is crucial. Several predictors have been made available and report high performance. Most of them evaluate their performance at every asparagine in protein sequences, not confined to asparagine in the N-X-S/T sequon. In this paper, we present N-GlyDE, a two-stage prediction tool trained on rigorously-constructed non-redundant datasets to predict N-linked glycosites in the human proteome. The first stage uses a protein similarity voting algorithm trained  on both glycoproteins and non-glycoproteins to predict a score for a protein to improve glycosite prediction. The second stage uses a support vector machine to predict N-linked glycosites by utilizing features of gapped dipeptides, pattern-based predicted surface accessibility, and predicted secondary structure. N-GlyDE's final predictions are derived from a weight adjustment of the second-stage prediction results based on the first-stage prediction score. Evaluated on N-X-S/T sequons of an independent dataset comprised of 53 glycoproteins and 33 non-glycoproteins, N-GlyDE achieves an accuracy and MCC of 0.740 and 0.499, respectively, outperforming the compared tools. The N-GlyDE web server is available at http://bioapp.iis.sinica.edu.tw/N-GlyDE/ .

GANA--a genetic algorithm for NMR backbone resonance assignment.

NMR data from different experiments often contain errors; thus, automated backbone resonance assignment is a very challenging issue. In this paper, we present a method called GANA that uses a genetic algorithm to automatically perform backbone resonance assignment with a high degree of precision and recall. Precision is the number of correctly assigned residues divided by the number of assigned residues, and recall is the number of correctly assigned residues divided by the number of residues with known human curated answers. GANA takes spin systems as input data and uses two data structures, candidate lists and adjacency lists, to assign the spin systems to each amino acid of a target protein. Using GANA, almost all spin systems can be mapped correctly onto a target protein, even if the data are noisy. We use the BioMagResBank (BMRB) dataset (901 proteins) to test the performance of GANA. To evaluate the robustness of GANA, we generate four additional datasets from the BMRB dataset to simulate data errors of false positives, false negatives and linking errors. We also use a combination of these three error types to examine the fault tolerance of our method. The average precision rates of GANA on BMRB and the four simulated test cases are 99.61, 99.55, 99.34, 99.35 and 98.60%, respectively. The average recall rates of GANA on BMRB and the four simulated test cases are 99.26, 99.19, 98.85, 98.87 and 97.78%, respectively. We also test GANA on two real wet-lab datasets, hbSBD and hbLBD. The precision and recall rates of GANA on hbSBD are 95.12 and 92.86%, respectively, and those of hbLBD are 100 and 97.40%, respectively.

HYPLOSP: a knowledge-based approach to protein local structure prediction.

Local structure prediction can facilitate ab initio structure prediction, protein threading, and remote homology detection. However, the accuracy of existing methods is limited. In this paper, we propose a knowledge-based prediction method that assigns a measure called the local match rate to each position of an amino acid sequence to estimate the confidence of our method. Empirically, the accuracy of the method correlates positively with the local match rate; therefore, we employ it to predict the local structures of positions with a high local match rate. For positions with a low local match rate, we propose a neural network prediction method. To better utilize the knowledge-based and neural network methods, we design a hybrid prediction method, HYPLOSP (HYbrid method to Protein LOcal Structure Prediction) that combines both methods. To evaluate the performance of the proposed methods, we first perform cross-validation experiments by applying our knowledge-based method, a neural network method, and HYPLOSP to a large dataset of 3,925 protein chains. We test our methods extensively on three different structural alphabets and evaluate their performance by two widely used criteria, Maximum Deviation of backbone torsion Angle (MDA) and Q(N), which is similar to Q(3) in secondary structure prediction. We then compare HYPLOSP with three previous studies using a dataset of 56 new protein chains. HYPLOSP shows promising results in terms of MDA and Q(N) accuracy and demonstrates its alphabet-independent capability.

HYPROSP: a hybrid protein secondary structure prediction algorithm--a knowledge-based approach.

We develop a knowledge-based approach (called PROSP) for protein secondary structure prediction. The knowledge base contains small peptide fragments together with their secondary structural information. A quantitative measure M, called match rate, is defined to measure the amount of structural information that a target protein can extract from the knowledge base. Our experimental results show that proteins with a higher match rate will likely be predicted more accurately based on PROSP. That is, there is roughly a monotone correlation between the prediction accuracy and the amount of structure matching with the knowledge base. To fully utilize the strength of our knowledge base, a hybrid prediction method is proposed as follows: if the match rate of a target protein is at least 80%, we use the extracted information to make the prediction; otherwise, we adopt a popular machine-learning approach. This comprises our hybrid protein structure prediction (HYPROSP) approach. We use the DSSP and EVA data as our datasets and PSIPRED as our underlying machine-learning algorithm. For target proteins with match rate at least 80%, the average Q3 of PROSP is 3.96 and 7.2 better than that of PSIPRED on DSSP and EVA data, respectively.

Symptom patterns and subgrouping of schizophrenic patients: significance of negative symptoms assessed on admission.

This study subgroups schizophrenic patients based on symptoms assessed on admission and examines the validity of the subgrouping using follow-up data and other clinical outcome variables. Schizophrenic patients (n=163) from consecutive admission received ratings on the positive and negative syndrome scale (PANSS) on admission and during a 1-year follow-up course. An exploratory graphic analysis on the admission PANSS derived four symptom dimensions: negative symptoms, disorganized thought, hostility/excitement and delusions/hallucinations. This yielded two subgroups of patients on admission, a group with marked negative (GWNEG) and a group without marked negative (GONEG) symptoms. Compared with the GONEG, the GWNEG had a poorer recovery rate, more impairment in attention, a slower response of the delusion/hallucination symptoms to neuroleptic treatment and a longer duration of index hospitalization. At a one-year follow-up, the GWNEG assessed on admission had persistently higher scores on the negative symptom and disorganized thought syndromes, less relapse rate, a shorter duration on job, as well as worse social functioning than the GONEG. Thus, the GONEG might comprise patients having a pure paranoid syndrome with quick and better treatment response, while the GWNEG comprises patients with the blunt-disorganization syndrome having a poorer outcome.

Detecting drug-drug interactions in medication profiles of psychiatric inpatients: a two-stage approach.

A two-stage evaluation methodology was developed to detect drug-drug interactions (D-DIs) of moderate and major severity in a psychiatric inpatient population. During a 3-month study period, the medication orders prescribed for 152 psychiatric inpatients in two psychiatric wards of a medical center were examined. A primary assessment of patients' reactions due to drug interactions was carried out by physicians who were provided with pertinent drug interaction monographs. A secondary assessment was performed by a three-member committee using explicit criteria to decide which reactions were actual D-DIs. Potential moderate or major severity D-DIs were identified in 79 (52.0%) of the 152 patients. A total of 130 potential interactions were detected in 339 (63.1%) of the 537 medication profiles. Actual D-DIs were seen in two patients (1.3%) who were prescribed trazodone with chlorpromazine and doxepin with paroxetine, respectively. The discrepancy between incidences of potential and actual D-DIs suggests that the method was sensitive in the detection and prudent in the assessment of D-DIs.

Amisulpride versus risperidone in the treatment of schizophrenic patients: a double-blind pilot study in Taiwan.

BACKGROUND AND PURPOSE: The atypical antipsychotics, amisulpride and risperidone, have different receptor affinity characteristics. Although the relative efficacy of both drugs compared to conventional antipsychotics is well established, it remains unclear how the efficacy of amisulpride compares with risperidone. There have been no controlled studies comparing amisulpride to risperidone in Asian patients. The purpose of this study was to compare the efficacy and safety of amisulpride with that of risperidone in Taiwanese schizophrenic patients. METHODS: Patients with productive positive symptoms (n = 48) were enrolled into this double-blind, randomized pilot study for 6 weeks. Patients received either amisulpride (400-800 mg/day) or risperidone (4-8 mg/day). Positive and Negative Syndrome Scale (PANSS), Clinical Global Impression (CGI), Social and Occupational Functioning Assessment Scale (SOFAS), and patients' subjective responses to treatment were assessed during the trial period. Adverse events were recorded at each follow-up visit. RESULTS: At the end of the trial, the mean dosage was 630 +/- 134 mg/day and 6.88 +/- 1.54 mg/day for amisulpride and risperidone, respectively. There was no significant difference in the reduction of the PANSS total score (amisulpride -24.1 versus risperidone -28.4, p = 0.999), the PANSS positive subscale score (amisulpride -6.8 versus risperidone -8.3, p = 0.467), the PANSS negative subscale score (amisulpride -5.6 versus risperidone -6.4, p = 0.999), or the CGI score between the two groups. The extrapyramidal symptom ratings, the improvement in the SOFAS (amisulpride 11.1 versus risperidone 10.0) and the subjective response (amisulpride 82% versus risperidone 83%) were comparable. No serious adverse events were recorded in either treatment group. There was a statistically significant body weight gain in the risperidone group. In contrast, there was a statistically, though not clinically, significant reduction of blood pressure and heart rate in the amisulpride group. CONCLUSIONS: This study suggests that amisulpride is as effective as risperidone in the treatment of patients with schizophrenia. Both drugs were well tolerated, but had different side effect profiles.

Human leukocyte antigen typing using a knowledge base coupled with a high-throughput oligonucleotide probe array analysis.

Human leukocyte antigens (HLA) are important biomarkers because multiple diseases, drug toxicity, and vaccine responses reveal strong HLA associations. Current clinical HLA typing is an elimination process requiring serial testing. We present an alternative in situ synthesized DNA-based microarray method that contains hundreds of thousands of probes representing a complete overlapping set covering 1,610 clinically relevant HLA class I alleles accompanied by computational tools for assigning HLA type to 4-digit resolution. Our proof-of-concept experiment included 21 blood samples, 18 cell lines, and multiple controls. The method is accurate, robust, and amenable to automation. Typing errors were restricted to homozygous samples or those with very closely related alleles from the same locus, but readily resolved by targeted DNA sequencing validation of flagged samples. High-throughput HLA typing technologies that are effective, yet inexpensive, can be used to analyze the world's populations, benefiting both global public health and personalized health care.

Improving the alignment quality of consistency based aligners with an evaluation function using synonymous protein words.

Most sequence alignment tools can successfully align protein sequences with higher levels of sequence identity. The accuracy of corresponding structure alignment, however, decreases rapidly when considering distantly related sequences (<20% identity). In this range of identity, alignments optimized so as to maximize sequence similarity are often inaccurate from a structural point of view. Over the last two decades, most multiple protein aligners have been optimized for their capacity to reproduce structure-based alignments while using sequence information. Methods currently available differ essentially in the similarity measurement between aligned residues using substitution matrices, Fourier transform, sophisticated profile-profile functions, or consistency-based approaches, more recently.In this paper, we present a flexible similarity measure for residue pairs to improve the quality of protein sequence alignment. Our approach, called SymAlign, relies on the identification of conserved words found across a sizeable fraction of the considered dataset, and supported by evolutionary analysis. These words are then used to define a position specific substitution matrix that better reflects the biological significance of local similarity. The experiment results show that the SymAlign scoring scheme can be incorporated within T-Coffee to improve sequence alignment accuracy. We also demonstrate that SymAlign is less sensitive to the presence of structurally non-similar proteins. In the analysis of the relationship between sequence identity and structure similarity, SymAlign can better differentiate structurally similar proteins from non- similar proteins. We show that protein sequence alignments can be significantly improved using a similarity estimation based on weighted n-grams. In our analysis of the alignments thus produced, sequence conservation becomes a better indicator of structural similarity. SymAlign also provides alignment visualization that can display sub-optimal alignments on dot-matrices. The visualization makes it easy to identify well-supported alternative alignments that may not have been identified by dynamic programming. SymAlign is available at http://bio-cluster.iis.sinica.edu.tw/SymAlign/.

Efficacy of valproate versus lithium in mania or mixed mania: a randomized, open 12-week trial.

The objective of this study was to compare the efficacy and safety of valproate and lithium in bipolar I patients experiencing a manic or a mixed episode. This international, randomized, open-label, parallel-group, equivalence study included 268 patients with bipolar I disorder. The starting dose of valproate was 20 mg/kg/day and that of lithium was 800 mg/day. Treatment duration was 12 weeks. The primary outcome measure was mean change in Young Mania Rating Scale score between baseline and study end. Secondary outcome measures were response and remission rates, change in Montgomery and Asberg Depression Rating Scale and Clinical Global Impression Bipolar Disorder instrument score, and occurrence of adverse events. The mean change from baseline in Young Mania Rating Scale score was 15.8+/-5.3 in the lithium group and 17.3+/-9.4 in the valproate group. The 90% confidence interval of the intergroup difference (-0.69; 3.31) was within prespecified equivalence limits. Response rates were 72.6% in the lithium group and 79.5% in the valproate group. Remission rates were 58.5 and 71.9%, respectively. No intergroup differences were observed in median time to treatment response (21 days) or change in Clinical Global Impression Bipolar Disorder instrument or Montgomery and Asberg Depression Rating Scale scores. Adverse events were reported in 42.8% of patients in the lithium group and 41.5% in the valproate group. Valproate and lithium showed comparable efficacy and tolerability in the treatment of acute mania over 12 weeks.