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Secondary structure predictions
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Lecture Bioinformatics I present the content: introduction to bioinformatics, sequence analysis, molecular evolution, RNA secondary structure prediction, protein sequences, protein structures, storage of biological sequence information, similarities & differences in sequences, introduction to alignment approaches, introduction to dynamic programming, dynamic programming methodology, moving from global to local alignment,...
320p
bachkhinhdaluu
03-12-2021
14
0
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Saintpaulia ionantha H. Wendl (synonym Streptocarpus ionanthus) has been considered an ornamental plant. The PHANTASTICA (PHAN) gene has a significant role in the formation of plant organs adaxial–abaxial polarity. In the present study the PHAN gene was identified in Saintpaulia for the first time using PCR. To determine the expression pattern of the PHAN gene, gene expression was compared at 3 developmental stages using real time PCR.
14p
tudichquannguyet
29-11-2021
3
0
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In this study, a new approach for ML-based miRNA prediction is proposed. Thousands of models are generated through classification of known human miRNAs and pseudohairpins with 3 classifiers: decision tree, naïve Bayes, and random forest. Although the method is based on human data, the best model was able to correctly assign 96% of nonhuman hairpins from MirGeneDB, suggesting that this approach might be useful for the analysis of miRNAs from other species.
13p
thiencuuchu
27-11-2021
15
1
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Comparative genomics approaches have facilitated the discovery of many novel non-coding and structured RNAs (ncRNAs). The increasing availability of related genomes now makes it possible to systematically search for compensatory base changes – and thus for conserved secondary structures – even in genomic regions that are poorly alignable in the primary sequence.
20p
viseulgi2711
31-08-2021
15
1
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MicroRNAs (miRNAs) constitute a well-known small RNA (sRNA) species with important regulatory roles. To date, several bioinformatics tools have been developed for large-scale prediction of miRNAs based on high-throughput sequencing data.
7p
vigiselle2711
30-08-2021
5
1
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As genome sequencing is becoming routine in biomedical research, the total number of protein sequences is increasing exponentially, recently reaching over 108 million. However, only a tiny portion of these proteins (i.e. ~75,000 or < 0.07%) have solved tertiary structures determined by experimental techniques.
12p
viwyoming2711
16-12-2020
11
1
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RNA secondary structure prediction, or folding, is a classic problem in bioinformatics: Given a sequence of nucleotides, the aim is to predict the base pairs formed in its three dimensional conformation. The inverse problem of designing a sequence folding into a particular target structure has only more recently received notable interest.
12p
viwyoming2711
16-12-2020
15
0
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With the advancement of next-generation sequencing and transcriptomics technologies, regulatory effects involving RNA, in particular RNA structural changes are being detected. These results often rely on RNA secondary structure predictions.
13p
viwyoming2711
16-12-2020
10
0
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Many content-based statistical features of secondary structural elements (CBF-PSSEs) have been proposed and achieved promising results in protein structural class prediction, but until now position distribution of the successive occurrences of an element in predicted secondary structure sequences hasn’t been used.
14p
viwyoming2711
16-12-2020
20
0
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Protein structures are flexible and often show conformational changes upon binding to other molecules to exert biological functions. As protein structures correlate with characteristic functions, structure comparison allows classification and prediction of proteins of undefined functions.
13p
vikentucky2711
26-11-2020
7
0
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RNA-binding proteins interact with specific RNA molecules to regulate important cellular processes. It is therefore necessary to identify the RNA interaction partners in order to understand the precise functions of such proteins.
11p
vikentucky2711
26-11-2020
13
0
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Improving accuracy and efficiency of computational methods that predict pseudoknotted RNA secondary structures is an ongoing challenge. Existing methods based on free energy minimization tend to be very slow and are limited in the types of pseudoknots that they can predict.
17p
vikentucky2711
26-11-2020
12
3
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Protein sequence alignment is essential for a variety of tasks such as homology modeling and active site prediction. Alignment errors remain the main cause of low-quality structure models. A bioinformatics tool to refine alignments is needed to make protein alignments more accurate.
7p
vikentucky2711
24-11-2020
13
1
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Alignment of large and diverse sequence sets is a common task in biological investigations, yet there remains considerable room for improvement in alignment quality. Multiple sequence alignment programs tend to reach maximal accuracy when aligning only a few sequences, and then diminish steadily as more sequences are added.
14p
vikentucky2711
24-11-2020
6
1
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The formation of contacts among protein secondary structure elements (SSEs) is an important step in protein folding as it determines topology of protein tertiary structure; hence, inferring inter-SSE contacts is crucial to protein structure prediction.
13p
vikentucky2711
24-11-2020
13
1
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RNA secondary structure around splice sites is known to assist normal splicing by promoting spliceosome recognition. However, analyzing the structural properties of entire intronic regions or pre-mRNA sequences has been difficult hitherto, owing to serious experimental and computational limitations, such as low read coverage and numerical problems.
20p
vioklahoma2711
19-11-2020
15
1
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According to structure-dependent function of proteins, two main challenging problems called Protein Structure Prediction (PSP) and Inverse Protein Folding (IPF) are investigated. In spite of IPF essential applications, it has not been investigated as much as PSP problem.
11p
vioklahoma2711
19-11-2020
5
1
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Protein secondary structure prediction (SSP) has been an area of intense research interest. Despite advances in recent methods conducted on large datasets, the estimated upper limit accuracy is yet to be reached.
18p
vioklahoma2711
19-11-2020
11
2
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SnoReport uses RNA secondary structure prediction combined with machine learning as the basis to identify the two main classes of small nucleolar RNAs, the box H/ACA snoRNAs and the box C/D snoRNAs.
14p
vioklahoma2711
19-11-2020
7
0
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RNA secondary structure prediction is a compute intensive task that lies at the core of several search algorithms in bioinformatics. Fortunately, the RNA folding approaches, such as the Nussinov base pair maximization, involve mathematical operations over affine control loops whose iteration space can be represented by the polyhedral model.
10p
viflorida2711
30-10-2020
19
2
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