Klasterduspõhine motiiviotsing lühikestel peptiididel
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2015
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Uute sekveneerimistehnoloogiate abil genereeritakse palju erineva taustaga bioloogilisi andmeid. Olulise info leidmiseks tuleb neid andmeid analüüsida. Antud töös koostame meetodi, mis suudab tuvastada motiive suurest hulgast lühikestest aminohapete järjestustest ehk peptiididest, mis sisaldavad infot konkreetse inimese organismis olevate antikehade kohta. On alust arvata, et leitud motiivide abil võib olla võimalik tuvastada, milliseid haiguseid inimene on põdenud. Kuna ükski uuritud olemasolevatest tööriistadest selle probleemi lahendamiseks ei sobinud, koostasime motiivide tuvastamiseks uue meetodi. Meetodi esimene osa, sarnaste peptiidigruppide tuvastamine, põhineb hierarhilisel klasterdamisel ning sisaldab kahte erinevat võimalust hierarhilise klasterduse puust automaatselt klastrite eraldamiseks. Meetodi teine osa on sarnaste peptiidide klastritest motiivide tuvastamine. Kuna pärisandmetes olevad motiivid ei ole teada, genereerisime sünteetilised andmed, mille peal koostatud meetodit valideerida. Koostatud meetod suutis vastavalt sünteetiliste andmete omadustele tuvastada 50% kuni 100% sinna sisestatud motiividest, pärisandmetele eeldatavalt kõige sarnasema andmestiku peal 86%. Motiivide lugemise meetod töötas samamoodi hästi, etteantud mürata klastrite pealt suudetakse tuvastada 100% motiividest ning müraga klastrite pealt 90% motiividest. Koostatud meetodit on võimalik rakendada ka teistest bioloogilistest andmetest motiivide otsimiseks. Sel juhul peaks muutma teatud parameetreid, mis selles töös kasutatava andmestiku jaoks on seatud. Edaspidiseks tööks võiks olla meetodi töötamise valideerimine teiste omadustega andmete peal.
With the help of new sequencing technologies we can generate a lot of biological data of different backgrounds. These data need to be analysed in order to extract the most important information from them. In this work we develop a method for extracting motifs from a large amount of short amino acid sequences called peptides that contain information about antibodies in that organism. Motifs found from these peptides could be linked to diseases that a person has had. Since none of the tested existing methods were suitable for solving this problem, we developed our own method that consists of two parts. First part, finding groups of similar peptides, is based on hierarchical clustering and has two different options for automatically extracting clusters from the hierarchical clustering tree. Second part is reading motifs from groups of similar peptides. Since we cannot validate the method on real data due to the lack of knowledge about the true motifs in them, we generate synthetic datasets that we validate the developed method on. The percentage of motifs the developed method could identify from synthetic data with different properties ranged from 50% to 100%, with 86% on the data that should be most similar to the real data. Method that reads motifs from group of similar peptides worked also very well. It could identify 100% of motifs from groups of peptides where no noise was added and 90% of motifs from noisier peptide groups. The developed method could be also used for motif discovery on different biological datasets. In that case we would have to change some parameters that were specifically chosen for this problem. Future work could be to test how well this method performs on different biological datasets.
With the help of new sequencing technologies we can generate a lot of biological data of different backgrounds. These data need to be analysed in order to extract the most important information from them. In this work we develop a method for extracting motifs from a large amount of short amino acid sequences called peptides that contain information about antibodies in that organism. Motifs found from these peptides could be linked to diseases that a person has had. Since none of the tested existing methods were suitable for solving this problem, we developed our own method that consists of two parts. First part, finding groups of similar peptides, is based on hierarchical clustering and has two different options for automatically extracting clusters from the hierarchical clustering tree. Second part is reading motifs from groups of similar peptides. Since we cannot validate the method on real data due to the lack of knowledge about the true motifs in them, we generate synthetic datasets that we validate the developed method on. The percentage of motifs the developed method could identify from synthetic data with different properties ranged from 50% to 100%, with 86% on the data that should be most similar to the real data. Method that reads motifs from group of similar peptides worked also very well. It could identify 100% of motifs from groups of peptides where no noise was added and 90% of motifs from noisier peptide groups. The developed method could be also used for motif discovery on different biological datasets. In that case we would have to change some parameters that were specifically chosen for this problem. Future work could be to test how well this method performs on different biological datasets.