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  • skblazer
    replied
    Thanks for your advise.

    I'll try.

    Leave a comment:


  • sdarko
    replied
    Originally posted by skblazer View Post
    Thanks

    My question is if I used blast/megablast, I'll get a lot of alignments without perfect aligning rate. How to set the cutoff to filter the output?

    For example, if one read's 6-26bp (27bp in total) can be aligned to a known tRNA, is it a candidate tRNA or not?

    For tRNA or rRNA, I should use a more relax alignment than miRNA, am I right?
    I've been using BLASTn to align snRNA reads to tRNA, rRNA, snoRNA, piwi associated RNA and miRNA.

    Basically, I first trim 3' adapter sequence and collapse the reads. Then I use BLASTn and use '-perc_identity 100' and '-word_size 16' so that at least 16 bases have to perfectly align to get a hit. When I'm parsing through my results I compare the length of the transcript and the length of the alignment. If they're the same, I call it a good alignment. If they're not, I set it aside to align to my next reference set.

    Leave a comment:


  • colindaven
    replied
    To the best of my knowledge rRNA and tRNAs are _more_ conserved than miRNAs. At least in bacteria we work on there is considerable variation in miRNAs between closely related species.
    I'm sure you can find some good references on this in pubmed though.

    Background MicroRNAs (miRNAs) are small RNAs (sRNA) ~21 nucleotides in length that negatively control gene expression by cleaving or inhibiting the translation of target gene transcripts. miRNAs have been extensively analyzed in Arabidopsis and rice and partially investigated in other non-model plant species. To date, 109 and 62 miRNA families have been identified in Arabidopsis and rice respectively. However, only 33 miRNAs have been identified from the genome of the model tree species (Populus trichocarpa), of which 11 are Populus specific. The low number of miRNA families previously identified in Populus, compared with the number of families identified in Arabidopsis and rice, suggests that many miRNAs still remain to be discovered in Populus. In this study, we analyzed expressed small RNAs from leaves and vegetative buds of Populus using high throughput pyrosequencing. Results Analysis of almost eighty thousand small RNA reads allowed us to identify 123 new sequences belonging to previously identified miRNA families as well as 48 new miRNA families that could be Populus-specific. Comparison of the organization of miRNA families in Populus, Arabidopsis and rice showed that miRNA family sizes were generally expanded in Populus. The putative targets of non-conserved miRNA include both previously identified targets as well as several new putative target genes involved in development, resistance to stress, and other cellular processes. Moreover, almost half of the genes predicted to be targeted by non-conserved miRNAs appear to be Populus-specific. Comparative analyses showed that genes targeted by conserved and non-conserved miRNAs are biased mainly towards development, electron transport and signal transduction processes. Similar results were found for non-conserved miRNAs from Arabidopsis. Conclusion Our results suggest that while there is a conserved set of miRNAs among plant species, a large fraction of miRNAs vary among species. The non-conserved miRNAs may regulate cellular, physiological or developmental processes specific to the taxa that produce them, as appears likely to be the case for those miRNAs that have only been observed in Populus. Non-conserved and conserved miRNAs seem to target genes with similar biological functions indicating that similar selection pressures are acting on both types of miRNAs. The expansion in the number of most conserved miRNAs in Populus relative to Arabidopsis, may be linked to the recent genome duplication in Populus, the slow evolution of the Populus genome, or to differences in the selection pressure on duplicated miRNAs in these species.

    "Our results suggest that while there is a conserved set of miRNAs among plant species, a large fraction of miRNAs vary among species"

    Ultimately you'll need to consider topics like thermodynamic stability, perhaps this link is helpful ?
    Background Previous studies have shown that microRNA precursors (pre-miRNAs) have considerably more stable secondary structures than other native RNAs (tRNA, rRNA, and mRNA) and artificial RNA sequences. However, pre-miRNAs with ultra stable secondary structures have not been investigated. It is not known if there is a tendency in pre-miRNA sequences towards or against ultra stable structures? Furthermore, the relationship between the structural thermodynamic stability of pre-miRNA and their evolution remains unclear. Results We investigated the correlation between pre-miRNA sequence conservation and structural stability as measured by adjusted minimum folding free energies in pre-miRNAs isolated from human, mouse, and chicken. The analysis revealed that conserved and non-conserved pre-miRNA sequences had structures with similar average stabilities. However, the relatively ultra stable and unstable pre-miRNAs were more likely to be non-conserved than pre-miRNAs with moderate stability. Non-conserved pre-miRNAs had more G+C than A+U nucleotides, while conserved pre-miRNAs contained more A+U nucleotides. Notably, the U content of conserved pre-miRNAs was especially higher than that of non-conserved pre-miRNAs. Further investigations showed that conserved and non-conserved pre-miRNAs exhibited different structural element features, even though they had comparable levels of stability. Conclusions We proposed that there is a correlation between structural thermodynamic stability and sequence conservation for pre-miRNAs from human, mouse, and chicken genomes. Our analyses suggested that pre-miRNAs with relatively ultra stable or unstable structures were less favoured by natural selection than those with moderately stable structures. Comparison of nucleotide compositions between non-conserved and conserved pre-miRNAs indicated the importance of U nucleotides in the pre-miRNA evolutionary process. Several characteristic structural elements were also detected in conserved pre-miRNAs.

    Leave a comment:


  • skblazer
    replied
    Because the organism I'm working on does not have a reference, I have to align them to some ncRNA database.

    I don't know the if the similarity of tRNA or rRNA among the organims is high as well as miRNA.

    Originally posted by bioinfosm View Post
    another option could be to align all reads to the genome without biasing to a selective reference dataset, and then use coordinates of your ncRNA database of interest, and identify which ones are expressed?

    Leave a comment:


  • bioinfosm
    replied
    another option could be to align all reads to the genome without biasing to a selective reference dataset, and then use coordinates of your ncRNA database of interest, and identify which ones are expressed?

    Leave a comment:


  • skblazer
    replied
    Thanks

    My question is if I used blast/megablast, I'll get a lot of alignments without perfect aligning rate. How to set the cutoff to filter the output?

    For example, if one read's 6-26bp (27bp in total) can be aligned to a known tRNA, is it a candidate tRNA or not?

    For tRNA or rRNA, I should use a more relax alignment than miRNA, am I right?

    Leave a comment:


  • dcfargo
    replied
    DSAP aligns to both miRBase (16) and RFam (10) if you have very short read data

    Leave a comment:


  • colindaven
    replied
    You'll need to mention what read length and system you used for the sequencing. Generally BLAST is too slow for "a lot of reads".

    Leave a comment:


  • skblazer
    started a topic How to align reads to known ncRNA database

    How to align reads to known ncRNA database

    I want to align a lot of reads to the known ncRNA database.

    I don't know which mapper is fit for small RNA alignment.

    blast? megablast? or bowtie ...

    how to set the parameter?

    Thanks

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