nadernamini/rna-seq_read-algn

RNA-Sequencing Read-Alignment

RNA-Sequencing Read-Alignment Project

Tiffany Chen and Nader Namini Asl

CS176 (Fall 2018)

Introduction

In this project, we have implemented some basic BWT functions that we covered in class, and then implemented a simplified version of an aligner for RNA sequencing reads.

Rules and Tips

• You can find all required (skeleton) functions/classes/methods in project.py. All the python files can be found in project.zip; We have created and imported other files for helper functions. The util files are in the same directory structure as project.py; Lastly, we included a brief write-up describing the approach we took for the project in write-up.pdf.
• Python 3 was used to develop the project and only the libraries and modules that are provided by the default installation of Python, as well as numpy, have been used.
• The input and outputs of the functions are specific in the docstring for each function.
• A Jupyter Notebook was used for loading in the data and developing/evaluating our alignment algorithm. You can find a working solution in the Python file Algorithm.ipynb.

BWT Functions

Rules and Tips

• The solutions are 0-indexed, and any intervals are [inclusive, exclusive).
• The algorithm was developed with the expectation that it will solely be tested on correctness on small or medium sized examples (probably length < 10000). Our function do not time out unless it really takes too long to run.
• We assumed an alphabet consisting of ['$', 'A', 'C', 'T', 'G'], where "$" will always only be the terminator.
• We assumed the s parameter in all the function is already terminated by "$".
• For constructing the suffix array, we did not implement the KS algorithm. From past experiences, the Python implementation of KS was actually slow as hell (probably because of the Python overhead). We used naive sorting on prefixes instead of radix sort. Our construction of the suffix array is efficient enough to use it for the aligner part of the project. Also, when we use our function for the aligner, it does not use a radix of over 100 for sorting, as we assume the memory usage is limited to something reasonable, not to mention that longer radixes take longer to generate as well.
• Do not delete docstring for exact suffix matches if you want a sanity check of the code.

Testing

We have provided a simple Python doctest as sanity check for the BWT functions. You can run this by running

python -m doctest project.py

on the command line.

Functions

• get_suffix_array(s)
  Naive implementation of suffix array generation (0-indexed). This code is fast enough so we have enough time in Aligner.__init__ (see bottom).

  Input:

  • s: a string of the alphabet ['A', 'C', 'G', 'T'] already terminated by a unique delimiter '$'

  Output:

  • list of indices representing the suffix array

  >>> get_suffix_array('GATAGACA$')
  [8, 7, 5, 3, 1, 6, 4, 0, 2]
  

• get_bwt(s, sa)
  Input:

  • s: a string terminated by a unique delimiter '$'
  • sa: the suffix array of s

  Output:

  • L: BWT of s as a string

• get_F(L)
  Input:

  • L = get_bwt(s)

  Output:

  • F, first column in Pi_sorted

• get_M(F)
  Returns the helper data structure M (using the notation from class). M is a dictionary that maps character strings to start indices. i.e. M[c] is the first occurrence of "c" in F.

  If a character "c" does not exist in F, we set M[c] = -1

• get_occ(L)
  Returns the helper data structure OCC (using the notation from class). OCC should be a dictionary that maps string character to a list of integers. If c is a string character and i is an integer, then OCC[c][i] gives the number of occurrences of character "c" in the bwt string up to and including index i.

• exact_suffix_matches(p, M, occ)
  Find the positions within the suffix array sa of the longest possible suffix of p that is a substring of s (the original string).

  Note that such positions must be consecutive, so we want the range of positions.

  Input:

  • p: the pattern string
  • M, occ: buckets and repeats information used by sp, ep

  Output:

  • a tuple (range, length)

    • range: a tuple (start inclusive, end exclusive) of the indices in sa that contains the longest suffix of p as a prefix. range=None if no indices matches any suffix of p
    • length: length of the longest suffix of p found in s. length=0 if no indices matches any suffix of p

    An example return value would be ((2, 5), 7). This means that p[len(p) - 7 : len(p)] is found in s and matches positions 2, 3, and 4 in the suffix array.

    >>> s = 'ACGT' * 10 + '$'
    >>> sa = get_suffix_array(s)
    >>> sa
    [40, 36, 32, 28, 24, 20, 16, 12, 8, 4, 0, 37, 33, 29, 25, 21, 17, 13, 9, 5, 1, 38, 34, 30, 26, 22, 18, 14, 10, 6, 2, 39, 35, 31, 27, 23, 19, 15, 11, 7, 3]
    >>> L = get_bwt(s, sa)
    >>> L
    'TTTTTTTTTT$AAAAAAAAAACCCCCCCCCCGGGGGGGGGG'
    >>> F = get_F(L)
    >>> F
    '$AAAAAAAAAACCCCCCCCCCGGGGGGGGGGTTTTTTTTTT'
    >>> M = get_M(F)
    >>> sorted(M.items())
    [('$', 0), ('A', 1), ('C', 11), ('G', 21), ('T', 31)]
    >>> occ = get_occ(L)
    >>> type(occ) == dict, type(occ['$']) == list, type(occ['$'][0]) == int
    (True, True, True)
    >>> occ['$']
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
    >>> exact_suffix_matches('ACTGA', M, occ)
    ((1, 11), 1)
    >>> exact_suffix_matches('$', M, occ)
    ((0, 1), 1)
    >>> exact_suffix_matches('AA', M, occ)
    ((1, 11), 1)
    

RNA Read Alignment

In this part of the project, we implemented some method to align RNA reads to genome. We are given a reduced size genome of roughly 11 million bases with the location of genes, isoforms, and exons. We used an alignment technique discussed in lecture 13 (TODO).

Reads

Our reads are generated from the transcriptome of the given genome. The given genome sequence corresponds to the forward strand; We assumed the genes/isoforms/exons are all on the forward strand, and all reads match to the forward strand. There are no insertions or deletions (but there could be mismatches) in the reads (i.e. each position in the read corresponds to some position in the genome). In addition, the genes/isoforms/exons that some reads are generated from have been hidden, i.e. we assumed these "unknown" genes will not be passed to our Aligner class in Aligner.__init__. In addition, some reads could be randomly generated. We have not tested against being able to align reads to the transcriptome if we are also not able to align these to the transcriptome. See Evaluation/Scoring for more details on what we have been evaluated on. In any read generated from the genome (visible or unknown), there are at most 6 mismatches, so any alignment with more than 6 mismatches is invalid.

Tips

• We only align to the forward strand, i.e. match the read sequence to the genome sequence, there should be no need to reverse complement anything.
• There is no insertion or deletion, so we do not output alignments with insertion or deletion.
• Ideally, we should first align the reads to the transcriptome then the genome.
• Implementing STAR, we assumeed minimum and maximum intron sizes to be 20 and 10000. We are using window sizes of 64000 bases from the anchor for our implementation, but we might have used whatever worked best.

Aligner Class

We implemented the Aligner class in project.py. It will be initialized with genome information, calling Aligner.align on a number of sequences to get our outputs, we can, then, evaluate the outputs.

Initialization/Constructor

In Aligner.__init__, we are given the genome sequence as well as genes, which is a list of Gene container objects that we have defined in shared.py. Each Gene contains a list of Isoform objects that correspond to the gene, and each Isoform contains a list of Exon objects. You could look at how these classes are defined in the file, but you cannot modify these classes.

Format of an Alignment

Since it has been specified that there is no insertion or deletion, an alignment of a read to the genome can be thought as k (usually k will be 1 or 2) separate ungapped alignments between the read and the genome (with some start index in the read and start index in the genome). Thus, we specified the alignment as a python list of k tuples of (<read_start_index>, <genome_start_index>, <length>). For example, an alignment of [(0, 2, 3), (3, 6, 10)] specifies that the 0th position of the read aligns to the 2nd position of the genome for 3 bases, and then the 3rd position of the read aligns to the 6th positon of the genome for 10 bases. If we can’t find an alignment for a read, we return an empty list, [].

Warning: if the ranges of two consecutive alignment pieces overlap in the read, i.e. if <read_start_1> + <length_1> > <read_start_2>, the second piece of the alignment will be discarded and the new alignment will be scored accordingly. This is checked for with the provided functions in evaluation.py (see Evaluation/Scoring).

Alignment Strategy

Our approach to the RNA sequencing project involved first alignment to the annotated transcriptome and then for the un-aligned reads, alignment to the genome. We created all the isoforms of all the genes and tried to align to the reads without any gaps, greedily choosing the mismatches, and bounding them at MAX_NUM_MISMATCHES. This meant that we checked the equality of the read sequence and each isoform element-by-element starting at index 0 and counted every inequality as a mismatch and terminated if the number of mismatches exceeded MAXNUM_MISMATCHES. This gave us a fast runtime that was on average less than 0.5s.

For the reads that did not align to the annotated transcriptome, we tried to align to the genome. After experimentation and evaluating our alignments with the alignment to hidden gene transcriptome, we found it best to limit the number of parts of our alignment to the genome to 1 as that would cover more than half of the hidden alignments. However, to find a single ungapped alignment for the reads to genome, we had to consider all the permutations of the read sequence. This meant that we went through the read sequence element by element and considered all the possible permutations of the bases at every index that would result in an exact match in the genome sequence. We achieved this by using a recursive function (find_max_so_far) that is documented in project.py along with all the helper functions used in the alignment.

Test Files

We are given files containing examples of what kind of genome and read sequences our alignment might be tested on. We parsed the files and tried our algorithm on these examples since they were representative of the evaluation set.

• genome.fa is a FASTA file with the genome sequence.

• reads.fa is a FASTA files with read sequences. Note that the file does not include base quality (PHRED) scores, as we have seen in the Bowtie1 algorithm. If we implemented Bowtie1, we can assume that the PHRED score is fixed for all bases.

• genes.tab is a tab-separated file containing three types of rows:

  • a gene row begins with "gene" and specifies the gene_id then a semicolon-separated list of isoform_id
  • an isoform row begins with "isoform" and specifies the isoform_id then a sorted semicolon-separated list of exon_id
  • an exon row begins with "exon" and specifies the exon_id, start, and end of the exon.

  Moreover, the genomic elements that are "unknown" (hidden when the Aligner is tested) are prefixed with "unknown". If a gene is unknown, its corresponding isoforms and exons will also be marked as unknown. We parse this file to construct our python set of genes that we feed into our Aligner.__init__. We made sure to construct each Gene with all of its corresponding Isoform objects, and etc.

Evaluation/Scoring

Alignment Prioritization

We always prioritize aligning reads to known isoforms (in genes.tab) with 6 or less mismatches. Only if we can’t align with 6 or less mismatches, we try to align reads to other parts of the genome with 6 or less mismatches (as these regions may represent unknown genes; there are no genes with unknown exons). We also minimize the number of mismatches, but do not choose an alignment to an unknown isoform with less mismatches over an alignment to a known isoform with more mismatches (but still 6 or less).

If we don’t align some indices of a read, those indices will be counted as mismatches (so it’s always best to align all indices of the read). Since there is no insertion or deletion, our alignment will be scored based on one-to-one and consecutive correspondence to the transcriptome. i.e. if we match a read to an isoform, we make sure that the read aligns consecutively to the transcript generated by concatenating the exons within the isoform, there are no gaps when aligning to the transcriptome.

evaluation.py

To make sure we are able to evaluate the aligner’s alignments, the function used to evaluate the alignments is provided in evaluation.py. We, first, need to construct a python set of known Isoform and a python set of unknown Isoform objects. We can, then, build an index by calling the index_isoform_locations function, and use this index to run evaluate_alignment on the alignment that we generate with Aligner.align.

Runtime Performance Requirement

For a 11 million base genome, our Aligner.__init__ method takes less than 500 seconds to run. Our Aligner.align method takes on average less than 0.5 seconds per read. In addition, we were penalized if our Aligner.align was more than 2 times slower than the mean runtime of everyone in the class.

Submission

This assignment was due on Wednesday 11/28 11:59pm PST. We created an Instructional account for CS 176 and submitted our project there. Only one person per our pair submitted, but we both submitted as well just to be safe. We, each, indicated the name of our partner when prompted by Glookup. As stated above, our submission keeps all required (skeleton) functions/classes/methods in project.py. All the python files can be found in project.zip; We have created and imported other files for helper functions. The util files are in the same directory structure as project.py. We may, depending on the reason, have another chance to submit with a penalty if our first submission crashes. Lastly, we included a brief write-up describing the approach we took for the project in write-up.pdf.

Feedback

Once the assignment was graded, a scores table for all students was released, with the follow information:

• SA: Suffix array subscore based on correctness.
• match: Exact suffix match subscore. Exact suffix matches were graded on correctness.
• alignment columns: Alignments with a reference aligner and our solution were generated, then both evaluated. The evaluations were either gene, hidden_gene, or unaligned. Let the rank of gene be 2, hidden_gene be 1, and unaligned be 0. Then for each read our score was [1 - 0.5 * (the aligner’s rank - our rank)]. Notice we get extra points if our rank > the aligner's rank.
  • fake_alignment: 75 totally random reads were generated. This column is an average our scores of those 75 reads. Everyone has a score of 1 in this column because our rank and the actual rank were all 0 for all the reads, since nothing was aligned to the genome.
  • gene_alignment: There are 1420 reads that are from genes that are given. This column is an average of our score for those reads.
  • hidden_gene_alignment: There are 80 reads generated from genes that are hidden. This column is an average of our score for those reads. 0.6125 is a common score (for people who didn’t align against the genome) because the actual aligner aligns 75% of the reads that come from hidden genes (which are not in the transcriptome), and if we only aligned against against the transcriptome, we will align 0% of these reads.
  • weighted_alignment: This is equal to 0.75 * gene_alignment + 0.2 * hidden_gene_alignment + 0.05 * fake_alignment
• setup_time: The amount of time it took to call Aligner.__init__. We got full credit if our_time < 500 seconds
• correctness_score: 0.15 * SA + 0.15 * match + 0.5 * weighted_alignment + 0.1 * (setup_time_under_threshold) + 0.1 * (alignment_time_under_threshold)
• overall_score: equal to correctness_score if we’re not late, else 0.85 * correctness_score