DeNovo assembly does not invariably translate input reads into a single contiguous sequence akin to a genomic consensus. Typically, errors in input data lead to fragmented sequences — referred to as contigs — which furthermore may overlap, thus encoding the same region of a genome more than once. Assembling a unified consensus sequence necessitates the systematic arrangement of these contigs while addressing discrepancies within overlapping regions. That is the Stitcher’s function.

Structure

The Stitcher is a specialized component within the MiCall system. It is designed to operate as an independent module which processes the assembled contigs, generally derived from DeNovo assembler outputs, and produce a singular, coherent sequence.

Modular Aspect

The Stitcher maintains a distinct and autonomous role within MiCall. Its implementation is fully isolated to the contig_stitcher*.py files within the MiCall’s source code. The stitcher module can be run as a CLI script, separately from the rest of the pipeline. The following command runs the Stitcher:

micall contig_stitcher --help

Interaction

Stitching is initiated either as a pipeline step in MiCall, or as a command line call given above. In each case:

Input: The Stitcher receives a single input file in CSV format. This file contains 1 or more contigs that are the outcomes of the previous assembly step, together with associated reference genome information. These contigs are essentially segments of DNA sequences. They can vary significantly in length.

Output: The sole output from the Stitcher is a CSV file. This file holds the stitched sequences – longer or fully continuous sequences that represent the genomic consensus formed by merging the initial fragmented contigs, and additional metadata, such as the inferred reference genome’s name.

Operational procedure

To clarify operations of the Stitcher, the subsequent section introduces a vocabulary that is necessary for a precise description.

Definitions

• An input nucleotide refers to a nucleotide of an initial assembly contig sequence.
• A reference nucleotide refers to a nucleotide of a reference genome sequence.
• A non-conflicting nucleotide is a reference nucleotide that has at most one candidate input nucleotide.
• A non-ambiguous nucleotide is an input nucleotide, which has a clear positioning with respect to all input nucleotides of all other contigs associated with the same reference genome. In particular, all conflicting nucleotides are ambiguous nucleotides because they do not have a clear positioning with respect to their competing conflicting nucleotide.
• An overlap is a continuos segement of conflicting nucleotides.
• Multidirectional alignment is a property of a contig such that:
  1. the contig has aligned in multiple parts.
  2. some parts have been aligned to the forward strand, and some to the reverse strand of the reference genome.
• Cross-alignment is a property of a contig such that:
  1. the contig has aligned in multiple parts.
  2. the contig-order of the aligned parts does not agree with the reference-order of the aligned parts.
• A non-aligned contig is a contig that has been assinged a reference sequence, but did not align to it.
• An invalid contig is a contig with multidirectional alignment.
• A stitched consensus is a valid contig in the output of the Stitcher.
• The final output refers to the contents of the only output CSV file produced by the Stitcher.

Principles

The reason the Stitcher operates effectively is due to its utilization of reference genomes as additional source of truth. More precisely, the Stitcher integrates two sets of data:

1. Sequences generated by the initial assembly.
2. Sequences of reference genomes to which assembled contigs get aligned.

We will say that 1. is the assembler’s data, and 2. is aligner’s.

The core belief is that a reference genome can be used to enhance the quality of and resolve conflicts within initial assembly contigs.

In applying this approach, the Stitcher is guided by the following principles:

Principle of Scale-Dependent Credibility

The reliability of sequence alignments increases as the length of the aligned segment increases. Therefore:

• Micro Scale: For shorter segments, assembler’s findings are more reliable, because of expected abundance of small, local mutations not present in the reference genome.

• Macro Scale: For longer segments, the aligner’s interpretations are prioritized. The exponential decrease in alignment errors with increased sequence length makes long alignments particularly trustworthy.

Principle of Length Prioritization

A longer contig typically arises from a greater number of reads spanning a larger genomic region. While this does not imply more reads per individual position, it suggests that the initial set of reads has successfully assembled over a more extensive sequence, reflecting a broader and more robust dataset. Moreover, aligning a longer sequence to the reference genome is statistically less probable, compared to a shorter sequence. This means that a successful alignment of a longer contig to the reference genome provides further confidence in its accuracy.

Therefore in scenarios where multiple contigs cover the same region of the reference genome, longer contigs are prioritized over shorter ones.

Ambiguity Omission Principle

To mitigate the potential propagation of uncertainties, any data that lacks a definitive, unambiguous position within the reference genome should be entirely excluded. This approach acknowledges that absolute certainty in complex genomic datasets is often unattainable, and tries to establish a reasonable default.

Regulations

Guided by the previously outlined principles, several precise regulations governing the Stitcher can be extracted:

1. For every reference genome, at most one stitched consensus must result.
2. No ambiguous, non-conflicting nucleotide shall be included into the final output.
3. Every non-conflicting- and non-ambiguous- nucleotide pertaining to a valid contig is required to be included in the stitched consensus for the associated reference genome.
4. The relative positions of non-conflicting- and non-ambiguous- nucleotides must be preserved in the final output.
5. All nucleotides present in the final output must exclusively originate from the initial assembly data.

Setup

The setup process for the Stitcher ensures that each contig is properly aligned and prepared for the stitching process. The steps are as follows:

1. Align Contigs: Align each contig to its corresponding reference genome to approximate their positions within a global reference framework, allowing for spatial comparison between different contigs.

2. Split Multi-Alignment Contigs: Split contigs that align to multiple distinct parts of the reference genome into separate segments.

3. Handle Reverse Complement: Reverse complement contigs that align to the reverse strand of the reference genome to ensure all sequences are oriented in the same direction.

4. Sort Contigs: Arrange the contigs based on their starting positions along the reference genome.

5. Group by Reference: Group contigs such that all contigs associated with the same reference genome are processed together.

These setup steps perform minimal alteration to the original contigs and are primarily guided by straightforward, logical considerations. Therefore, they do not require extensive rationalization compared to the subsequent rules.

Rules of operation

Stitching is an iterative process, governed by the following rules:

Rule 1: Merge Non-Overlapping Contigs

1. Verify Non-Overlap: Ensure that the end of the first contig is less or equal to the start of the second contig according to their positions on the reference genome.

2. Delete adjacent non-aligned parts: Filter out any non-aligned nucleotides positioned after the first contig’s aligned part and before the second contig’s aligned part.

3. Concatenate Sequences: Directly join the end of the first contig to the start of the second contig.

Example:

Input:

• Contig 1: Sequence = GG[ATGCCC]AA, aligned to Referece X at position 10, with first two and last two nucleotides not aligned.
• Contig 2: Sequence = AC[TTAG]TA, aligned to Referece X at position 30, with first two and last two nucleotides not aligned.

Procedure:

• Verify that Contig 1 ends before Contig 2 begins.
• Delete non-aligned nucleotides resulting in Contig 1 = GG[ATGCCC] and Contig 2 = [TTAG]TA.
• Concatenate Contig 1 and Contig 2 to form GG[ATGCCC][TTAG]TA.

Result:

• The new sequence, GG[ATGCCCTTAG]TA, spans positions 10 to 34 on the reference genome.

Rationale

There isn’t many alternative actions available to us in these circumstances. This enables us to consider all of them:

1. Leaving contigs as separate:

   Separate contigs would result in multiple consensus outputs for one genome. Thus it fails to comply with regulation 1.

2. Omitting the strip step:

   Note that the adjacent non-aligned nucleotides of the two sequences are ambiguous, non-conflicting nucleotides. Therefore, leaving them in place violates regulation 2.

3. Introducing additional modifications:

   Since given contigs do not overlap, every nucleotide in them is non-conflicting. Additionally, we have stripped all the ambiguous nucleotides. Therefore, all modifications that can be introduced would either violate regulation 3, regulation 4 or regulation 5.

Rule 2: Merge Overlapping Contigs

1. Verify Overlap: Check if the ending position of the first contig is greater than the starting position of the second contig.

2. Delete adjacent non-aligned parts: Filter out any non-aligned nucleotides positioned after the first contig’s aligned part and before the second contig’s aligned part.

3. Align Overlapping Regions:
   • Extract the sequences from the overlapping region in both contigs.
   • Use a global alignment method to align these overlapping sub-sequences.
4. Calculate Concordance Scores:
   • Compute concordance scores for each position within the overlapping region. Importantly, the concordance calculation is done purely between the aligned overlapping subsequences of the contigs, with no regard to the reference genome sequence. The concordance score represents how well the nucleotides from the two contigs match at each position.
   • The score is calculated using a sliding average approach, emphasizing regions with high sequence agreement.
5. Determine Optimal Cut Point:
   • Identify the cut point based on the concordance scores such that the it lies in the middle of regions with the highest concordance.
   • This means making cuts as far away from disagreeing nucleotides as possible.
6. Segment and Combine:
   • Segment the overlapping sequences at the determined cut point.
   • Concatenate the non-overlapping parts of the contigs with the segmented parts from the overlapping region.

Example

Input:

• Contig 1: Sequence = G[GGCC A--TAC]T T, aligned to Reference X from positions 10 to 19.
• Contig 2: Sequence = --CCAC[AAATAC C]GGG, aligned to Reference X from positions 14 to 20.

Procedure:

1. Verify Overlap:
   • Contig 1 ends at position 19, and Contig 2 starts at position 14 (both on Reference X), resulting in an overlap from positions 14 to 19.

2. Delete adjacent non-aligned parts: Contig 1 is right-stripped to become G[GGCCA--TAC], contig B is left-stripped to become [AAATACC]GGG.

3. Align Overlapping Regions:
   • The overlaping sequence is A--TAC from contig A, and AAATAC from contig B.
   • Align them globally to produce the following alignments: --ATAC and AAATAC
4. Calculate Concordance:
   • Calculate concordance scores for positions 15 to 20, considering only the overlap between the two aligned sequences.
   • Approximate concordance: [0.1, 0.2, 0.3, 0.8, 0.8, 0.3].
5. Determine Cut Point:
   • Use the computed concordance scores to identify the cut point.
   • In this example, the highest concordance scores are around positions with the score 0.9, so choose it as the cut point.

   Aligned sequences:
   
   A: --ATAC
   B: AAATAC
   
   Concordance:
   0.1 0.2 0.3 0.8 0.8 0.3
   
   Based on the concordance, cut between the positions:
   A: --AT|AC
   B: AAAT|AC
   

6. Segment and Combine:
   • Cut the sequences at the determined cut points.
   • Combine sequence parts: G[GGCC][--AT][AC][C]GGG.

Result:

• The new sequence G[GGC--ATACC]GGG spans positions 10 to 20 on Reference X, representing the most accurate combined sequence.

Rationale

This rule is similar to Rule 1, but deals with overlapping regions. When contigs overlap, there is a need to choose a cut point due to:

1. Aligner Constraints: The aligner constrains the size of the overlapping sequence (by the Principle of Scale-Dependent Credibility), making it impossible to keep both versions of the overlapping region simultaneously.
2. Small scale adjustments: Overlaps are usually small enough that assembler data is the highest quality data we have for the nucleotide positions within it. Thus interleaving segments from both contigs would again violate the Principle of Scale Dependent Credibility.

We base the choice on concordance scores, which measure the degree of agreement between the overlapping sequences of the two contigs. We look for the highest concordance because:

Choice of Cut Point:

• If a cut point is chosen where concordance is lower than the maximum, it implies that in the neighboring region around the cut point, either to the left or right, there will almost certainly be some incorrect nucleotides due to disagreement between the contigs.
• Conversely, if the concordance is high at the chosen cut point, the neighboring region is similar between the two contigs. The selected extensions (left of the cut point from the left contig and right of the cut point from the right contig) are longer than the alternative from the conflicting contig, ensuring greater trust in these regions based on their length (by the Principle of Length Prioritization).

While this method of choosing the cut point based on concordance scores aligns with the Principles, we acknowledge that there might be other ways to determine the optimal cut point. However, given the complexity of overlapping regions and the necessity to preserve relative ordering, this concordance-based approach is the best we have identified so far.

Rule 3: Split Contigs with Gaps Which Are Covered by Other Contigs

1. Identify Large Gaps:
   • For each contig, identify regions within its alignment to the reference genome that lack coverage, i.e., gaps. Both small gaps resulting from sequencing errors and large gaps are recognized.
   • Significant gaps are determined based on a pre-defined threshold. In the context of HIV genome analysis, a gap size of greater than 21 nucleotides is considered significant due to common RNA secondary structure phenomena.
2. Verify Coverage by Other Contigs:
   • For each identified significant gap, check if other contigs span or cover this gap. Specifically, check if other contigs have aligned reference coordinates that overlap with the coordinates of the gap.
3. Split Contig at Gap Midpoint:
   • If a significant gap is covered by another contig, split the contig containing the gap into two separate contigs at the midpoint of the gap.
   • Left-trim the new right contig segment and right-trim the new left contig segment to remove ambiguity from their ends.
4. Update Contig List:
   • Replace the original contig with its two new segments in the list of contigs.

Example

Input:

• Contig 1: Sequence = AGC[TTAC---------------------GGCACATATCATA]CTA, aligned to Reference X from positions 10 to 48.
• Contig 2: Sequence = G[TGAC-----GGACG-TCGTCG--TACGATCAG]G, aligned to Reference X from positions 8 to 40.

Procedure:

1. Identify Large Gaps:
   • Contig 1 has a significant gap between positions 14 and 35.
2. Verify Coverage by Other Contigs:
   • Contig 2 covers the gap region from positions 8 to 40.
3. Split Contig at Gap Midpoint:
   • Split Contig 1 into two parts at the midpoint of the gap (i.e., position 24). This creates two new contigs:
     • Contig 1a: Sequence = AGC[TTAC----------], aligned to Reference X from positions 10 to 24.
     • Contig 1b: Sequence = [-----------GGCACATATCATA]CTA, aligned to Reference X from positions 25 to 48.
   • Trim the new segments:
     • Contig 1a becomes AGC[TTAC].
     • Contig 1b becomes [GGCACATATCATA]CTA.
4. Update Contig List:
   • Discard the original Contig 1 and add Contig 1a and Contig 1b to the list of contigs.

Result:

• Modified list of contigs now includes Contig 2, Contig 11, and Contig 12.

Rationale

The decision to split contigs at large gaps covered by other contigs is grounded in the Principle of Scale-Dependent Credibility. Assemblers can occasionally join sequence fragments incorrectly if the end of one segment appears similar to the start of another. Relying on the aligner’s macro-scale credibility helps identify these erroneous joins. Large gaps within a contig are suspicious and suggest potential assembler errors, whereas small gaps are generally due to sequencing errors or micro-scale mutations and do not warrant splitting. By leveraging the aligner’s high reliability on a macro scale, we can effectively pinpoint these errors. If other contigs cover large gaps, it confirms the aligner’s indication that the assembly might have joined unrelated segments. Splitting contigs at the midpoint of significant gaps ensures that only those segments supported by both the assembler’s micro-scale data and the aligner’s macro-scale alignment are included in the final stitched consensus.

The threshold for considering a gap significant is set at 21 nucleotides. This value was chosen because it correlates with the average pitch of the RNA helix, which reflects how reverse transcription periodic deletions are structured around 21 nucleotides in HIV sequences. Choosing this cutoff recognizes that deletions of approximately this length are a common feature due to RNA secondary structures and should not automatically warrant a split. This way, we avoid splitting on every small gap, which is expected given the nature of micro-scale mutations, but effectively identify and act on larger, suspect gaps indicative of potential assembler errors.

Rule 4: Discard Contigs That Are Fully Covered By Other Contigs

1. Identify Covered Contigs:
   • For each contig in the input set, calculate its aligned interval on the reference genome.
   • Identify intervals (regions) that are completely covered by input contigs.
2. Compare Intervals:
   • Assess the intervals of each contig to find any contig that falls entirely within the span of other contig intervals. These are the contigs that are fully covered by others.
3. Discard Fully Covered Contigs:
   • Once identified, remove the covered contigs.

Example

Input:

• Contig 1: Sequence = A[ATCGA]GCT, aligned to Reference X from positions 10 to 15.
• Contig 2: Sequence = C[TAGTTG]A, aligned to Reference X from positions 14 to 19.
• Contig 3: Sequence = G[CGTACC]G, aligned to Reference X from positions 12 to 17.

Procedure:

1. Identify Covered Contigs:
   • Calculate the intervals:
     • Contig 1: [10-15]
     • Contig 2: [14-19]
     • Contig 3: [12-17]
2. Compare Intervals:
   • Assess intervals and find Contig 3: [12-17] is completely within the intervals [10-15] of Contig 1 and [14-19] of Contig 2.
3. Discard Fully Covered Contigs:
   • Remove Contig 3 from the analysis.

Result:

• Unchanged remaining contigs Contig 1 and Contig 3.

Rationale

The underlying idea for this rule is founded on the two following principles:

1. Principle of Length Prioritization: longer contigs are inherently more reliable.

2. Principle of Scale-Dependent Credibility: Fully covered contigs might introduce small-scale inconsistencies that the longer contig can resolve more credibly, given the enhanced reliability associated with its length and alignment.

Moreover, keeping all contigs would violate Regulation 1.

Note: rules apply to contigs that are in the same group.

Diagnostics

The Stitcher includes diagnostic tools to ensure transparency and correctness throughout the stitching process. Two primary methods are used for diagnostics: visualizer plots and traditional log files. These tools help users understand and verify the decisions made by the Stitcher during the stitching process.

The Optional Visualizer Tool

The visualizer can be enabled through the --plot flag when running the Stitcher executable. Running the Stitcher with this flag will produce an SVG file that visualizes the stitching process, helping to confirm and debug the Stitcher’s operations.

To use the visualizer, run the Stitcher with an additional argument specifying the path to the output plot file. Here’s an example of how to stitch contigs and retrieve a visualizer plot:

PYTHONPATH="/path/to/micall/repository" python3 -m micall.core.contig_stitcher "contigs.csv" "stitched_contigs.csv" --plot "visualized.svg"

Command Line Arguments:

• contigs.csv: Input file in CSV format containing assembled contigs and related information.
• stitched_contigs.csv: Output CSV file that will contain the stitched contigs.
• --plot visualized.svg: The optional argument to generate a visual representation of the stitching process, saved as visualized.svg.

Understanding the Output

In practice, a visualizer plot might look something like this:

From such a diagram, you can gain insights into the following aspects of the stitching process:

• Reference genome: The best matching reference genome for this group of contigs was determined to be HIV1-A1-RW-KF716472.

• Dropped Contigs: Contigs that were dropped due to being fully covered by other contigs, as per Rule 4. In the example plot:
  • Contigs 2, 4, 7, 8, and 6 were dropped.
• Split Contigs: Contigs split at large gaps covered by other contigs, according to Rule 3. The resulting parts are shown as individual segments.
  • Contig 1 was split around Contig 3, producing segments labeled as 1.1 and 1.3.
• Joined Contigs: Contigs that were merged due to overlap:
  • Contigs 1 and 3, which were joined as per Rule 2, with ambiguous, non-conflicting nucleotides discarded, shown as segments labeled 1.2 and 3.1.
• Unaligned Contigs: Contigs that failed to align to the reference genome during the alignment step of the setup.
  • Contig 5 failed to align.
• Contigs without a Reference: Contigs for which a reference genome could not be determined during the reference detection step of the setup.
  • Contigs 9 and 10 failed to determine a reference genome.

Understanding these basics will help to interpret other scenarios displayed by the visualizer plot.

Traditional Logs

In addition to visual tools, the Stitcher produces traditional log files that provide textual details of the stitching process. These logs are crucial for debugging and understanding the sequence of operations performed by the Stitcher. The verbosity of logs can be adjusted using command-line options (--verbose, --debug, --quiet).

Here is an example of typical log entries:

DEBUG:micall.core.contig_stitcher:Introduced contig 'contig.00001' (seq = TA...CA) of ref 'HIV1-C-BR-JX140663-seed', group_ref HIV1-A1-RW-KF716472-seed (seq = GA...AC), and length 7719.
DEBUG:micall.core.contig_stitcher:Introduced contig 'contig.00002' (seq = CG...AG) of ref 'HIV1-A1-RW-KF716472-seed', group_ref HIV1-A1-RW-KF716472-seed (seq = GA...AC), and length 1634.
...
DEBUG:micall.core.contig_stitcher:Contig 'contig.00006' produced 1 aligner hits. After connecting them, the number became 1.
DEBUG:micall.core.contig_stitcher:Part 0 of contig 'contig.00006' re-aligned as (5) at 7M...3D@[8,1433]->[7461,8946].
DEBUG:micall.core.contig_stitcher:Part 0 of contig 'contig.00007' aligned at 76M...3D@[0,732]->[5536,6277].
DEBUG:micall.core.contig_stitcher:Contig 'contig.00007' produced 1 aligner hits. After connecting them, the number became 1.
DEBUG:micall.core.contig_stitcher:Part 0 of contig 'contig.00007' re-aligned as (6) at 76M...3D@[0,732]->[5536,6277].
...
DEBUG:micall.core.contig_stitcher:Ignored insignificant gap of (5), 3D@[790,789]->[8280,8282].
DEBUG:micall.core.contig_stitcher:Ignored insignificant gap of (5), 19D@[1324,1323]->[8817,8835].
DEBUG:micall.core.contig_stitcher:Ignored insignificant gap of (5), 2D@[1354,1353]->[8866,8867].
...
DEBUG:micall.core.contig_stitcher:Created contigs (8) at 24M...1I@[14,3864]->[0,4558] and (9) at 708D...92I@[3865,7691]->[4559,9032] by cutting (1) at 24M...1I@[14,7691]->[0,9032] at cut point = 4558.5.
DEBUG:micall.core.contig_stitcher:Doing rstrip of (8) at 24M...1I@[14,3864]->[0,4558] (len 7719) resulted in (10) at 24M...1I@[14,3864]->[0,3850] (len 3865).
DEBUG:micall.core.contig_stitcher:Doing lstrip of (9) at 708D...92I@[3865,7691]->[4559,9032] (len 7719) resulted in (11) at 14M...1I@[0,3734]->[5267,9032] (len 3762).
DEBUG:micall.core.contig_stitcher:Split contig (1) at 24M...1I@[14,7691]->[0,9032] around its gap at [3864, 3863]->[3851, 5266]. Left part: (10) at 24M...1I@[14,3864]->[0,3850], right part: (11) at 14M...1I@[0,3734]->[5267,9032].
...
DEBUG:micall.core.contig_stitcher:Created a frankenstein (34) at 24M...1I@[14,4185]->[0,4171] (len 4186) from [(26) at 24M...1I@[14,3041]->[0,3027] (len 3042), (28) at 271M2D3M2I395M@[0,670]->[3028,3698] (len 671), (30) at 152M@[0,151]->[3699,3850] (len 152), (31) at 321M@[0,320]->[3851,4171] (len 321)].
DEBUG:micall.core.plot_contigs:Contig name (26) is displayed as '1.1'.
DEBUG:micall.core.plot_contigs:Contig name (36) is displayed as '1.3'.
DEBUG:micall.core.plot_contigs:Contig name 'contig.00002' is displayed as '2'.
DEBUG:micall.core.plot_contigs:Contig name (2) is displayed as '2'.
DEBUG:micall.core.plot_contigs:Contig name 'contig.00003' is displayed as '3'.
DEBUG:micall.core.plot_contigs:Contig name (31) is displayed as '3.2'.
DEBUG:micall.core.plot_contigs:Contig name 'contig.00004' is displayed as '4'.
DEBUG:micall.core.plot_contigs:Contig name (4) is displayed as '4'.
DEBUG:micall.core.plot_contigs:Contig name 'contig.00005' is displayed as '5'.
DEBUG:micall.core.plot_contigs:Contig name 'contig.00006' is displayed as '6'.
DEBUG:micall.core.plot_contigs:Contig name (5) is displayed as '6'.
DEBUG:micall.core.plot_contigs:Contig name 'contig.00007' is displayed as '7'.
DEBUG:micall.core.plot_contigs:Contig name (6) is displayed as '7'.
DEBUG:micall.core.plot_contigs:Contig name 'contig.00008' is displayed as '8'.
DEBUG:micall.core.plot_contigs:Contig name (7) is displayed as '8'.
DEBUG:micall.core.plot_contigs:Contig name 'contig.00009' is displayed as '9'.
DEBUG:micall.core.plot_contigs:Contig name 'contig.00010' is displayed as '10'.

The following points illustrate how these logs can facilitate understanding the stitching process:

• Contig Introduction: Provides details about the contigs introduced for stitching.
  • Introduced contig 'contig.00001'...
• Alignment Details: Shows the alignment results for each contig.
  • Part 0 of contig 'contig.00006' re-aligned as (5) at 7M...3D@[8,1433]->[7461,8946].
• Gap Handling: Indicates which gaps were ignored as insignificant.
  • Ignored insignificant gap of (5), 3D@[790,789]->[8280,8282].
• Splitting and Merging Contigs: Documents the splitting of contigs at identified gaps and merging of overlapping segments.
  • Split contig (1) at 24M...1I@[14,7691]->[0,9032]...
  • Created a frankenstein (34) at 24M...1I@[14,4185]->[0,4171]...
• Visualizer Compatibility: The visualizer diagrams are produced exclusively from these logs, ensuring compatibility and consistency between the logs and visual output.

Limitations

Following limitations stem from the choice of principles and various assumptions that guide the Stitcher’s operation. Understanding them allows users to better interpret the results and apply post-processing steps to mitigate potential issues.

One of the critical challenges is the handling of ambiguous nucleotides. The Stitcher’s Ambiguity Omission Principle, which aims to avoid propagating uncertainties, might lead to the exclusion of significant sequence data, resulting in the loss of potentially valuable variations or mutations.

Moreover, the calculation of concordance in overlapping regions assumes that local concordance is the best indicator of the correct sequence. This approach may not fully account for complex genomic rearrangements or context outside the overlap, potentially compromising the accuracy of the stitched sequence.

The predefined threshold for significant gaps, based on specific assumptions about RNA secondary structures of organisms like HIV, might not generalize well to other organisms or genomic regions. This can lead to over-splitting or under-splitting contigs, further fragmenting the consensus sequence.

Additionally, The Stitcher’s principle of scale-dependent credibility might overlook important small-scale variations, such as single nucleotide polymorphisms (SNPs) or small indels, especially if they are lost in longer contigs deemed more reliable.

Another critical limitation arises in the context of pipelines dealing with proviral sequences. The Stitcher might attempt to “fix” sequences that are inherently “broken”, such as those that are scrambled, contain long deletions, or exhibit hypermutation. In such cases, the tool’s corrective measures may not be desirable, as they risk introducing inaccuracies. This limitation makes the Stitcher unsuitable for certain pipelines where the integrity of such broken sequences should be preserved without alteration.

Finally, the handling of multidirectional and cross-alignments may fall short when addressing complex genomic rearrangements, such as translocations or inversions, potentially resulting in misalignments and stitching errors in the consensus sequence.