Fragment Collapsing and Splitting While Assembling High-Resolution Restriction Maps

Gillett, Will D.; Daues, Jim; Hanks, Liz; Capra, Robert

doi:10.1089/cmb.1995.2.185

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…The automatic phase of the MCD map assembly proceeds as a series of steps during which the order of the clone ends and restriction fragments are progressively refined (16,17). Fragment sizing outliers are handled by the ''gray zone'' concept.…”

Section: Resultsmentioning

confidence: 99%

“…Every fragment-size list is referred to as one ''enzyme domain,'' regardless of whether it results from a single-enzyme digest or a two-enzyme double digest. Reconstruction of the underlying fragment ordering and synchronization of this information across enzyme domains is accomplished with the software package DNAM (16,17). High sampling redundancies of ϫ15-30 are required, not only for closure of the maps, but also for ordering of the fragments and automatic detection of bad data.…”

mentioning

confidence: 99%

“…The gel-image analysis system (G.K.-S.W., unpublished work) performs automatic lane finding, laneprofile generation, peak detection, size calibration, band quantitation, and fragment-count estimation. Results are written out as lists of fragment sizes, with the vector fragments flagged, and passed to the map assembly system, DNAM (16,17). The output from DNAM is passed to a third software package, ATLAS (E.C.T., unpublished work), which performs stringent quality control checks and produces a graphical representation of the MCD map.…”

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confidence: 99%

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Multiple-complete-digest restriction fragment mapping: Generating sequence-ready maps for large-scale DNA sequencing

Wong

Thayer

et al. 1997

Proc. Natl. Acad. Sci. U.S.A.

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Multiple-complete-digest mapping is a DNA mapping technique based on complete-restriction-digest fingerprints of a set of clones that provides highly redundant coverage of the mapping target. The maps assembled from these fingerprints order both the clones and the restriction fragments. Maps are coordinated across three enzymes in the examples presented. Starting with yeast artificial chromosome contigs from the 7q31.3 and 7p14 regions of the human genome, we have produced cosmid-based maps spanning more than one million base pairs. Each yeast artificial chromosome is first subcloned into cosmids at a redundancy of ؋15-30. Complete-digest fragments are electrophoresed on agarose gels, poststained, and imaged on a f luorescent scanner. Aberrant clones that are not representative of the underlying genome are rejected in the map construction process. Almost every restriction fragment is ordered, allowing selection of minimal tiling paths with clone-to-clone overlaps of only a few thousand base pairs. These maps demonstrate the practicality of applying the experimental and software-based steps in multiple-complete-digest mapping to a target of significant size and complexity. We present evidence that the maps are sufficiently accurate to validate both the clones selected for sequencing and the sequence assemblies obtained once these clones have been sequenced by a ''shotgun'' method.

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Section: Resultsmentioning

confidence: 99%

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Multiple-complete-digest restriction fragment mapping: Generating sequence-ready maps for large-scale DNA sequencing

Wong

Thayer

et al. 1997

Proc. Natl. Acad. Sci. U.S.A.

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“…Methods for filtering the data have also been offered as an attempt to remove typical errors such as false positives or chimeric clones [GDHC95,MGL94]. Further pointers to the literature are given in [VLM96].…”

Section: Related Workmentioning

confidence: 99%

A branch-and-cut approach to physical mapping with end-probes

Christof

Jünger

Kececioglu

et al. 1997

Proceedings of the First Annual International Conference on Computational Molecular Biology - RECOMB '97

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A fundamental problem in computational biology is the construction of physical maps of chromosomes from hybridiz;c tion experiments between unique probes and clones of chromosome fragments in the presence of error. Alizadeh, Karp, Weisser and Zweig (AKWZ94] first considered a maximumlikelihood model of the problem that is equivalent to finding an o&ring of the probes that minimizes a weighted sum of errors, and developed several effective heuristics. We show that by exploiting information about the endprobes of clones, this model can be formulated as a weighted Betweenness Problem. Thii affords the signiicant advautage of allowing the well-developed tools of integer lmearprogramming aud branch-and-cut algorithms to be brought to bear on physical mapping, enabling us for the first time to solve small mapping instances to optima&y even in the presence of high error. We also show that by combining the optimal solution of many small overlapping Betweenness Problems, one can effectively screen errors from larger instances, and solve the edited instance to optimality as a Hamming-Distance Traveling Salesman Problem. This suggests a new combined approach to physical map construction.

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