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Alejandro A. Schäffer

Researcher at National Institutes of Health

Publications -  269
Citations -  98821

Alejandro A. Schäffer is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Cancer & Population. The author has an hindex of 74, co-authored 249 publications receiving 92583 citations. Previous affiliations of Alejandro A. Schäffer include Rice University & Bell Labs.

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Journal ArticleDOI

Evaluating annotations of an Agilent expression chip suggests that many features cannot be interpreted.

TL;DR: Expression array studies should evaluate the annotations of reporters and remove those reporters that have suspect annotations, but one must recognize that data sources are frequently updated leading to slightly changing validation results over time.
Journal ArticleDOI

First radiation hybrid map of the river buffalo X chromosome (BBUX) and comparison with BTAX.

TL;DR: Although the set of mapped markers does not cover the entire X chromosome, this map is a starting point for the construction of a high-resolution map, which is necessary for characterization of small rearrangements that might have occurred between the Bubalus bubalis and Bos taurus X chromosomes.
Journal ArticleDOI

Construction of a river buffalo (Bubalus bubalis) whole-genome radiation hybrid panel and preliminary RH mapping of chromosomes 3 and 10

TL;DR: The RH maps presented here are the starting point for mapping additional loci, in particular, genes and expressed sequence tags that will allow detailed comparative maps between buffalo, cattle and other species to be constructed.
Journal ArticleDOI

FISHtrees 3.0: Tumor Phylogenetics Using a Ploidy Probe.

TL;DR: Tests on simulated data and on real data further demonstrate novel insights these methods offer into tumor progression processes and validate FISHtrees 3.0, which implements a ploidy-based tree building method based on mixed integer linear programming (MILP).
Book ChapterDOI

Making the shortest-paths approach to sum-of-pairs multiple sequence alignment more space efficient in practice

TL;DR: A much more detailed description of MSA, one of the few existing programs that attempts to find optimal alignments of multiple protein or DNA sequences, is given and substantial improvements in the time and space usage are made.