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1. Remove duplicates rm_dup\remove_dup_PE.py
2. Split libraries based on barcode.txt split_library_pairend.py
3. Recover fragment for each library recoverFragment
4. Split partners and classify different types of fragments. split_partner.py
5. Algn to the genome for both parts of "paired" fragments. Stitch-seq_Aligner.py
6. Determine the RNA types of different parts within fragment. RNA_composition.py
7. (Alternative) Find liner sequences within the library. find_linker_new.py
8. Determine strong interactions from output of step 5. Select_strongInteraction_pp.py for parallel computing; Select_strongInteraction.py regular.

we can also do bed annotation on different cis-features. bed_annotation.py

1. NNNXXXXNN - miRNA - AUC UGG UAA UCC GUA UAA AGU AUG UUG AUG UUC CAA UAA GCA GAU CAU GUU UUU UAA GCC GUC A - mRNA
2. NNNXXXXNN - miRNA - UAA GCA GAU CAU GUU UUU UAA GCC GUC A - mRNA
3. NNNXXXXNN - miRNA - AUC UGG UAA UCC GUA UAA AGU AUG UUG AUG UUC CAA - mRNA
4. NNNXXXXNN - miRNA - AUC UGG UAA UCC GUA UAA AGU AUG UUG AUG UUC CAA
5. NNNXXXXNN - UAA GCA GAU CAU GUU UUU UAA GCC GUC A - mRNA
6. NNNXXXXNN - mRNA
7. NNNXXXXNN - miRNA (less likely)

linkers:

• UAA GCA GAU CAU GUU UUU UAA GCC GUC A
• AUC UGG UAA UCC GUA UAA AGU AUG UUG AUG UUC CAA

* type1:

                                                        (reverse primer)
       forward reads:                      XXXX...XXXXNAGATCGGAAGAGCGGTTCAG
                                           ||||...||||
       reverse reads: TGTGCTGCGAGAAGGCTAGANXXXX...XXXX
                       (forward primer)

* type2:

       forward reads: XXXXX...XXXXXXXXXXX...XXXX
                                     ||||...||||
       reverse reads:                XXXX...XXXXXXXXXXX...XXXX

1. python libraries [python 2.x]:
   ..* Biopython
   ..* Pysam
   ..* BAM2X
   ..* numpy, scipy
   ..* parallel python (only for Select_strongInteraction_pp.py)
   ..* PyCogent (for annotation of RNA types) [see Notes]

2. Boost.Python