HH-MOTiF: de novo detection of short linear motifs in proteins by Hidden Markov Model comparisons

Prytuliak, Roman; Volkmer, Michael; Meier, Markus; Habermann, Bianca

doi:10.1093/nar/gkx341

Cited by 13 publications

(9 citation statements)

References 28 publications

(33 reference statements)

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“…For instance, one can assess the variance of performance across different groups or to look at the correlations of performance with internal group characteristics (e.g., group size or average benchmark CSE length). Such an analysis can, for example, show that a predictor works well only with relatively large group sizes (as was shown for SLiMFinder [ 29 ] in [ 17 ]) or only with long CSEs (as was shown for MEME [ 30 ]). Furthermore, due to the diverse set of proposed measures one can easily see non-optimal aspects of a predictor (e.g., high PPV in combination low TPR would imply consistent under-prediction, while high ACC with low F1 would unravel the vulnerability to the false positive paradox while working on unbalanced data).…”

Section: Discussionmentioning

confidence: 91%

“…The first case study deals with the annotation of proteins. It analyses some details of the performance of our previously published method HH-MOTiF, a de novo motif predictor [ 17 ]. We also compare the functionality of SLALOM to other available tools by addressing specific questions within this case study.…”

Section: Resultsmentioning

confidence: 99%

“…A previous version of SLALOM was used in an earlier publication [ 17 ] to assess the performance of different methods for de novo motif prediction in protein sequences, and to compare them between each other. In brief, we used experimentally validated motifs stored in the ELM database to develop, optimize and test the HH-MOTiF algorithm.…”

Section: Resultsmentioning

confidence: 99%

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SLALOM, a flexible method for the identification and statistical analysis of overlapping continuous sequence elements in sequence- and time-series data

2018

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BackgroundProtein or nucleic acid sequences contain a multitude of associated annotations representing continuous sequence elements (CSEs). Comparing these CSEs is needed, whenever we want to match identical annotations or integrate distinctive ones. Currently, there is no ready-to-use software available that provides comprehensive statistical readout for comparing two annotations of the same type with each other, which can be adapted to the application logic of the scientific question.ResultsWe have developed a method, SLALOM (for StatisticaL Analysis of Locus Overlap Method), to perform comparative analysis of sequence annotations in a highly flexible way. SLALOM implements six major operation modes and a number of additional options that can answer a variety of statistical questions about a pair of input annotations of a given sequence collection. We demonstrate the results of SLALOM on three different examples from biology and economics and compare our method to already existing software. We discuss the importance of carefully choosing the application logic to address specific scientific questions.ConclusionSLALOM is a highly versatile, command-line based method for comparing annotations in a collection of sequences, with a statistical read-out for performance evaluation and benchmarking of predictors and gene annotation pipelines. Abstraction from sequence content even allows SLALOM to compare other kinds of positional data including, for example, data coming from time series.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2020-x) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 91%

Section: Resultsmentioning

confidence: 99%

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SLALOM, a flexible method for the identification and statistical analysis of overlapping continuous sequence elements in sequence- and time-series data

2018

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“…Truncation of the Euc1 CC domain abolished the interaction with Slx5 (Fig EV3B) as well as Euc1 dimerization in Y2H (Appendix Fig S5C and D); however, Slx5 interaction was also lost by truncation of the region between aa 140 and 183, indicating a potential Slx5‐binding site in this region (compare Fig EV3B and Appendix Fig S5C, Euc1 1–140). To guide our search, we used HH‐MOTiF for de novo motif prediction (Prytuliak et al , ) using Euc1 aa 81–183 and a set of putative Slx5 substrates as query. We introduced mutations in predicted binding sites downstream of the CC domain: Two of these strongly diminished binding to Slx5 while leaving dimerization intact (Slx5‐binding mutant 1 and 2, SBM1: aa 139–143 ENQKK>ANAAA, SBM2: aa 162–165 KEVF>AAAA, Fig D and Appendix Fig S6A and B).…”

Section: Resultsmentioning

confidence: 99%

Slx5/Slx8‐dependent ubiquitin hotspots on chromatin contribute to stress tolerance

et al. 2019

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Chromatin is a highly regulated environment, and protein association with chromatin is often controlled by post‐translational modifications and the corresponding enzymatic machinery. Specifically, SUMO ‐targeted ubiquitin ligases ( STU bLs) have emerged as key players in nuclear quality control, genome maintenance, and transcription. However, how STU bLs select specific substrates among myriads of SUMO ylated proteins on chromatin remains unclear. Here, we reveal a remarkable co‐localization of the budding yeast STU bL Slx5/Slx8 and ubiquitin at seven genomic loci that we term “ubiquitin hotspots”. Ubiquitylation at these sites depends on Slx5/Slx8 and protein turnover on the Cdc48 segregase. We identify the transcription factor‐like Ymr111c/Euc1 to associate with these sites and to be a critical determinant of ubiquitylation. Euc1 specifically targets Slx5/Slx8 to ubiquitin hotspots via bipartite binding of Slx5 that involves the Slx5 SUMO ‐interacting motifs and an additional, novel substrate recognition domain. Interestingly, the Euc1‐ubiquitin hotspot pathway acts redundantly with chromatin modifiers of the H2A.Z and Rpd3L pathways in specific stress responses. Thus, our data suggest that STU bL‐dependent ubiquitin hotspots shape chromatin during stress adaptation.

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“…Understanding the protein sequence is crucial for biological processes discovery. HH-MOTiF (Prytuliak et al, 2017) is one method for short linear motifs detection. In this method, the motif root is chosen as a template to align the motif leaves using HMMs.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

CFSP: a collaborative frequent sequence pattern discovery algorithm for nucleic acid sequence classification

Peng

2020

PeerJ

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Background Conserved nucleic acid sequences play an essential role in transcriptional regulation. The motifs/templates derived from nucleic acid sequence datasets are usually used as biomarkers to predict biochemical properties such as protein binding sites or to identify specific non-coding RNAs. In many cases, template-based nucleic acid sequence classification performs better than some feature extraction methods, such as N-gram and k-spaced pairs classification. The availability of large-scale experimental data provides an unprecedented opportunity to improve motif extraction methods. The process for pattern extraction from large-scale data is crucial for the creation of predictive models. Methods In this article, a Teiresias-like feature extraction algorithm to discover frequent sub-sequences (CFSP) is proposed. Although gaps are allowed in some motif discovery algorithms, the distance and number of gaps are limited. The proposed algorithm can find frequent sequence pairs with a larger gap. The combinations of frequent sub-sequences in given protracted sequences capture the long-distance correlation, which implies a specific molecular biological property. Hence, the proposed algorithm intends to discover the combinations. A set of frequent sub-sequences derived from nucleic acid sequences with order is used as a base frequent sub-sequence array. The mutation information is attached to each sub-sequence array to implement fuzzy matching. Thus, a mutate records a single nucleotide variant or nucleotides insertion/deletion (indel) to encode a slight difference between frequent sequences and a matched subsequence of a sequence under investigation. Conclusions The proposed algorithm has been validated with several nucleic acid sequence prediction case studies. These data demonstrate better results than the recently available feature descriptors based methods based on experimental data sets such as miRNA, piRNA, and Sigma 54 promoters. CFSP is implemented in C++ and shell script; the source code and related data are available at https://github.com/HePeng2016/CFSP.

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HH-MOTiF: de novo detection of short linear motifs in proteins by Hidden Markov Model comparisons

Cited by 13 publications

References 28 publications

SLALOM, a flexible method for the identification and statistical analysis of overlapping continuous sequence elements in sequence- and time-series data

SLALOM, a flexible method for the identification and statistical analysis of overlapping continuous sequence elements in sequence- and time-series data

Slx5/Slx8‐dependent ubiquitin hotspots on chromatin contribute to stress tolerance

CFSP: a collaborative frequent sequence pattern discovery algorithm for nucleic acid sequence classification

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