Basic assembly statistics

• N25: the number $L$ s.t. 25% of all bases in the assembly are in a contig of length less than $L$.
• N50: the number $L$ s.t. 50% of all bases in the assembly are in a contig of length less than $L$.
• N75: the number $L$ s.t. 75% of all bases in the assembly are in a contig of length less than $L$.
• longest_length: the length of the longest contig in the assembly.
• mean_length: the mean contig length in the assembly.
• median_length: the median contig length in the assembly.
• shortest_length: the length of the shortest contig in the assembly.
• num_contigs: the number of contigs in the assembly.
• num_contigs_longer_than_$n$_bp: the number of contigs in the assembly longer than $n$ bp, for $n$ = 100 and 300.

Precision-recall statistics

Let $A$ be an assembly and $B$ be an oracleset. By $s\in A$, we mean that $s$ is a contig in $A$. By $s[i]$ we mean the $i$th base in $s$.

Fix min_frac_identity parameter $\alpha$ $\geq$ 0.8. If desired, fix max_frac_indel parameter $\beta$.

We use the following procedure to compute nucleotide-level precision and recall statistics.

• If $\beta$ is not given, set $\beta=1$.
• Run blat -minIdentity=80 B.fa A.fa A_to_B.psl to align the sequences in $A$ to those in $B$, excluding those alignments with less than 80% identity according to blat.
• Compute the following quantities based on these alignments:
  • num_mismatching_bases: Total number of mismatching (nonidentical) bases in the alignments. If this is too high, something might be wrong.
  • num_raw_matching_bases: Total number of matching (identical) bases in all the alignments, with bases counted multiple times if they are involved in multiple alignments.
  • num_uniq_matching_bases: Total number of bases in B that match at least one aligned base in A. Each base in B is counted only once, no matter how many bases in A it aligns to.
• We compute these quantities as follows:
  • For each $t\in B$, initialize mask($t$) as a string of all 0's, the same length as $t$.
  • Initialize num_raw_matching_bases = num_uniq_matching_bases = num_mismatching_bases = 0.
  • For each alignment $q \to t$ where $q\in A$ and $t\in B$:
    • If num_identity($q\to t$)/length'($q$) < $\alpha$ or if num_indel($q\to t$)/length'($q$) > $\beta$, then skip this alignment. (Here, length'($q$) is the number of non-$\mathtt N$ bases in $q$.)
    • Otherwise: We can think of the alignment $q\to t$ as a set of pairs $(k,l)$ where $k$ indexes $q$ and $l$ indexes $t$. For each pair $(k,l)$ in the alignment:
      • If $q[k] = t[l]$ and $t[l] \neq \mathtt N$:
        • Increment num_raw_matching_bases.
        • If mask($t$)$[l] = 0$:
          • Increment num_uniq_matching_bases.
          • Set mask($t$)$[l] = 1$.
      • Else: Increment num_mismatching_bases.
• Let num_bases_in_$A = \sum_{q\in A}$ length($q$) and num_bases_in_$B = \sum_{t\in B}$ length($t$). (Here, length($q$) is the number of bases in $q$, including $\mathtt N$'s.)
• Let nucl_precision_at_$\alpha$_and_$\beta$ = num_uniq_matching_bases / num_bases_in_$A$.
• Let nucl_recall_at_$\alpha$_and_$\beta$ = num_uniq_matching_bases / num_bases_in_$B$.

• If $\beta$ is not given, set $\beta=1$.
• Run blat -minIdentity=80 A.fa B.fa B_to_A.psl to align the sequences in $B$ to those in $A$, excluding those alignments with less than 80% identity according to blat.
• Compute the following quantities based on these alignments:
  • num_raw_alignments: Total number of alignments.
  • num_uniq_alignments: Number of elements of B that are involved in at least one alignment.
• We compute these quantities as follows:
  • For each $q \in B$, initialize mask($q$) = 0.
  • Initialize num_raw_alignments = num_uniq_alignments = 0.
  • For each alignment $q \to t$ where $q\in B$ and $t\in A$:
    • If num_identity($q\to t$)/length'($q$) < $\alpha$ or if num_indel($q\to t$)/length'($q$) > $\beta$, then skip this alignment. (Here, length'($q$) is the number of non-$\mathtt N$ bases in $q$.)
    • Otherwise: Mark the current oracleset element $q$ as having an alignment, and increment the number of oracleset elements with alignments as appropriate. In detail:
      • Increment num_raw_alignments.
      • If mask($q$) = 0:
        • Increment num_uniq_alignments.
        • Set mask($q$) = 1.
• Let num_contigs_in_$A = |A|$ and num_contigs_in_$B = |B|$.
• Let transcript_precision_at_$\alpha$_and_$\beta$ = num_uniq_alignments / num_contigs_in_$A$.
• Let transcript_recall_at_$\alpha$_and_$\beta$ = num_uniq_alignments / num_contigs_in_$B$.

RSEM approx columns

RSEM eval columns

RSEM prior columns

RSEM ss columns