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Quantifying the similarity of topological domains across normal and cancer human cell types
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| Zeitschriftentitel: | Bioinformatics |
|---|---|
| Personen und Körperschaften: | , |
| In: | Bioinformatics, 34, 2018, 13, S. i475-i483 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
|
| Schlagwörter: |
| author_facet |
Sauerwald, Natalie Kingsford, Carl Sauerwald, Natalie Kingsford, Carl |
|---|---|
| author |
Sauerwald, Natalie Kingsford, Carl |
| spellingShingle |
Sauerwald, Natalie Kingsford, Carl Bioinformatics Quantifying the similarity of topological domains across normal and cancer human cell types Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| author_sort |
sauerwald, natalie |
| spelling |
Sauerwald, Natalie Kingsford, Carl 1367-4803 1367-4811 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/bty265 <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>Three-dimensional chromosome structure has been increasingly shown to influence various levels of cellular and genomic functions. Through Hi-C data, which maps contact frequency on chromosomes, it has been found that structural elements termed topologically associating domains (TADs) are involved in many regulatory mechanisms. However, we have little understanding of the level of similarity or variability of chromosome structure across cell types and disease states. In this study, we present a method to quantify resemblance and identify structurally similar regions between any two sets of TADs.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>We present an analysis of 23 human Hi-C samples representing various tissue types in normal and cancer cell lines. We quantify global and chromosome-level structural similarity, and compare the relative similarity between cancer and non-cancer cells. We find that cancer cells show higher structural variability around commonly mutated pan-cancer genes than normal cells at these same locations.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>Software for the methods and analysis can be found at https://github.com/Kingsford-Group/localtadsim</jats:p> </jats:sec> Quantifying the similarity of topological domains across normal and cancer human cell types Bioinformatics |
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10.1093/bioinformatics/bty265 |
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| title |
Quantifying the similarity of topological domains across normal and cancer human cell types |
| title_unstemmed |
Quantifying the similarity of topological domains across normal and cancer human cell types |
| title_full |
Quantifying the similarity of topological domains across normal and cancer human cell types |
| title_fullStr |
Quantifying the similarity of topological domains across normal and cancer human cell types |
| title_full_unstemmed |
Quantifying the similarity of topological domains across normal and cancer human cell types |
| title_short |
Quantifying the similarity of topological domains across normal and cancer human cell types |
| title_sort |
quantifying the similarity of topological domains across normal and cancer human cell types |
| topic |
Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| url |
http://dx.doi.org/10.1093/bioinformatics/bty265 |
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2018 |
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i475-i483 |
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<jats:title>Abstract</jats:title>
<jats:sec>
<jats:title>Motivation</jats:title>
<jats:p>Three-dimensional chromosome structure has been increasingly shown to influence various levels of cellular and genomic functions. Through Hi-C data, which maps contact frequency on chromosomes, it has been found that structural elements termed topologically associating domains (TADs) are involved in many regulatory mechanisms. However, we have little understanding of the level of similarity or variability of chromosome structure across cell types and disease states. In this study, we present a method to quantify resemblance and identify structurally similar regions between any two sets of TADs.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Results</jats:title>
<jats:p>We present an analysis of 23 human Hi-C samples representing various tissue types in normal and cancer cell lines. We quantify global and chromosome-level structural similarity, and compare the relative similarity between cancer and non-cancer cells. We find that cancer cells show higher structural variability around commonly mutated pan-cancer genes than normal cells at these same locations.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Availability and implementation</jats:title>
<jats:p>Software for the methods and analysis can be found at https://github.com/Kingsford-Group/localtadsim</jats:p>
</jats:sec> |
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| description | <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>Three-dimensional chromosome structure has been increasingly shown to influence various levels of cellular and genomic functions. Through Hi-C data, which maps contact frequency on chromosomes, it has been found that structural elements termed topologically associating domains (TADs) are involved in many regulatory mechanisms. However, we have little understanding of the level of similarity or variability of chromosome structure across cell types and disease states. In this study, we present a method to quantify resemblance and identify structurally similar regions between any two sets of TADs.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>We present an analysis of 23 human Hi-C samples representing various tissue types in normal and cancer cell lines. We quantify global and chromosome-level structural similarity, and compare the relative similarity between cancer and non-cancer cells. We find that cancer cells show higher structural variability around commonly mutated pan-cancer genes than normal cells at these same locations.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>Software for the methods and analysis can be found at https://github.com/Kingsford-Group/localtadsim</jats:p> </jats:sec> |
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| spelling | Sauerwald, Natalie Kingsford, Carl 1367-4803 1367-4811 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/bty265 <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>Three-dimensional chromosome structure has been increasingly shown to influence various levels of cellular and genomic functions. Through Hi-C data, which maps contact frequency on chromosomes, it has been found that structural elements termed topologically associating domains (TADs) are involved in many regulatory mechanisms. However, we have little understanding of the level of similarity or variability of chromosome structure across cell types and disease states. In this study, we present a method to quantify resemblance and identify structurally similar regions between any two sets of TADs.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>We present an analysis of 23 human Hi-C samples representing various tissue types in normal and cancer cell lines. We quantify global and chromosome-level structural similarity, and compare the relative similarity between cancer and non-cancer cells. We find that cancer cells show higher structural variability around commonly mutated pan-cancer genes than normal cells at these same locations.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>Software for the methods and analysis can be found at https://github.com/Kingsford-Group/localtadsim</jats:p> </jats:sec> Quantifying the similarity of topological domains across normal and cancer human cell types Bioinformatics |
| spellingShingle | Sauerwald, Natalie, Kingsford, Carl, Bioinformatics, Quantifying the similarity of topological domains across normal and cancer human cell types, Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| title | Quantifying the similarity of topological domains across normal and cancer human cell types |
| title_full | Quantifying the similarity of topological domains across normal and cancer human cell types |
| title_fullStr | Quantifying the similarity of topological domains across normal and cancer human cell types |
| title_full_unstemmed | Quantifying the similarity of topological domains across normal and cancer human cell types |
| title_short | Quantifying the similarity of topological domains across normal and cancer human cell types |
| title_sort | quantifying the similarity of topological domains across normal and cancer human cell types |
| title_unstemmed | Quantifying the similarity of topological domains across normal and cancer human cell types |
| topic | Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| url | http://dx.doi.org/10.1093/bioinformatics/bty265 |