A new approach to assessing the validity of indels in algorithmic pair alignments

Polyanovsky, V. O.; Roytberg, Mikhail A.; Tumanyan, V. G.

doi:10.1134/s0006350908040015

Cited by 2 publications

(6 citation statements)

References 6 publications

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“…More importantly, just considering indel length without evaluating multiplicity does not serve to achieve substantially increased Precision. Here, multiplicity is decisive which in particular confirms the findings on twilight zone alignments reported in [25]. In the Twi alignments Precision can be raised up to about 0.7.…”

Section: Discussion Of Resultssupporting

confidence: 89%

“…They also observe a downward bias in the number of inferred indels which is due to effects termed gap attraction and gap annihilation. Decreased numbers of inferred indels were equally observed in other recent studies [17,25]. This points out that numbers and size of computationally inferred indels can make statements about alignment quality.…”

Section: Introductionsupporting

confidence: 78%

“…4. Thereby we deliver statistical evidence of that computational alignments are biased in terms of numbers and sizes of gaps as described in [19,25]. In particular too little numbers of gaps can reflect alignment artifacts.…”

Section: Summary Of Contributionsmentioning

confidence: 68%

“…Increased similarity Sim(x, y), however, indicates that already shorter and less gaps are more likely to reflect true indels simply because an alignment of high similarity is an overall more trustworthy statement. In summary, we provide a statistically sound, systematic approach to answering questions such as "Am I to believe that 4 gaps of size at least 6 in an alignment of length 200 and similarity 50 are likely to reflect true indels" as motivated by the recent studies [17,19,25]. We opted to address these questions for score-based alignments with affine gap costs for two reasons:…”

Section: Introductionmentioning

confidence: 99%

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Pair HMM Based Gap Statistics for Re-evaluation of Indels in Alignments with Affine Gap Penalties

Schönhuth

Salari

Şahinalp

2010

Lecture Notes in Computer Science

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Although computationally aligning sequence is a crucial step in the vast majority of comparative genomics studies our understanding of alignment biases still needs to be improved. To infer true structural or homologous regions computational alignments need further evaluation. It has been shown that the accuracy of aligned positions can drop substantially in particular around gaps. Here we focus on reevaluation of score-based alignments with affine gap penalty costs. We exploit their relationships with pair hidden Markov models and develop efficient algorithms by which to identify gaps which are significant in terms of length and multiplicity. We evaluate our statistics with respect to the well-established structural alignments from SABmark and find that indel reliability substantially increases with their significance in particular in worst-case twilight zone alignments. This points out that our statistics can reliably complement other methods which mostly focus on the reliability of match positions.

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Section: Discussion Of Resultssupporting

confidence: 89%

Section: Introductionsupporting

confidence: 78%

Section: Summary Of Contributionsmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pair HMM Based Gap Statistics for Re-evaluation of Indels in Alignments with Affine Gap Penalties

Schönhuth

Salari

Şahinalp

2010

Lecture Notes in Computer Science

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“…Gap attraction and gap annihilation occur because of using affine gap penalty function in dynamic program and cause a downward bias in the number of inferred indels [20], [40].…”

Section: Introductionmentioning

confidence: 99%

A Preliminary Study for Solving the Gap Problems in Protein Sequence Alignment

El-Lakkani

El-Sherif

2023

Preprint

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Protein sequence alignment has many applications in molecular biology. The existing algorithms produce imperfect alignments, because of two factors: 1) the proposed gap penalty function and its parameter values 2) the gap problems. The present work is an attempt to obtain correct alignments using a novel alignment method based on the harmonic identical words present in the sequences to be aligned. To prove the accuracy of this approach, our method is applied to three pairs of sequences. The results are compared with the corresponding results obtained from three alignment programs; the EMBL-EBI EMBOSS Needle program with minimum number of gaps, the MSAEMBL-EBI MUSCLE and Clustal Omega programs. The comparison shows that, in case of highly similar protein sequences the identity and the maximum score value of our alignment method is higher than that of the EMBL-EBI EMBOSS Needle, MUSCLE and Clustal Omega programs. In case of distantly related sequences, the maximum score value of our alignment is much higher than that of the EMBL-EBI EMBOSS Needle, MUSCLE and Clustal Omega programs and the obtained percent identity is shifted from the twilight zone to the safe zone.Our results also show that the gap lengths distribution follows a power law, and this is not achieved by many current alignment programs.

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A new approach to assessing the validity of indels in algorithmic pair alignments

Cited by 2 publications

References 6 publications

Pair HMM Based Gap Statistics for Re-evaluation of Indels in Alignments with Affine Gap Penalties

Pair HMM Based Gap Statistics for Re-evaluation of Indels in Alignments with Affine Gap Penalties

A Preliminary Study for Solving the Gap Problems in Protein Sequence Alignment

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