should i biology major get a mac or a pc

#SHOULD I BIOLOGY MAJOR GET A MAC OR A PC SERIES#

AFM's Quickview function replaces expression ratios with a corresponding range of colors, thereby transforming an array database into a color representation that can be easily manipulated as an Excel file. Microarray data are much easier to visualize as colored squares, where the color and color intensity indicate whether a gene is induced or repressed, and the extent of any induction and repression, respectively. Huge columns of numbers are mind-numbing to most users. Starting with a database of microarray experiments, AFM quickly prepares input files for either program. Although these are not difficult to prepare, a user can waste hours reformatting his/her data into these set file formats.

Each of these freeware packages requires data to be in a distinct file format. Two excellent clustering freeware programmes are available: Cluster and Support Vector Machine. ClusteringĬlustering is a powerful method for discovering patterns in array data. All steps required to determine the expression ratios from microarray experiments - for instance spot finding, spot quantitation, background subtraction, normalization, and so on - must be done prior to using AFM 4.0. AFM 4.0 is thus compatible with data from spotted cDNA, spotted oligonucleotide, or Affymetrix microarrays. The input file for AFM 4.0 is any database of expression ratios. We have primarily used AFM to manage array data from yeast, but the program can be easily tailored for use with data sets from any organism, including large mammalian array files (D. The programs are written in Microsoft Visual Basic and are used within the familiar confines of Microsoft Excel, allowing the researcher to use standard Excel functions in addition to AFM-specific ones.

#SHOULD I BIOLOGY MAJOR GET A MAC OR A PC SERIES#

We have developed a series of programs, collectively packaged as Array File Maker 4.0 (AFM), that rapidly manipulate data, prepare files for use in freeware clustering programs, and make figures. Querying the data set often becomes rate-limiting, as sifting through and rearranging millions of data points is a monotonous and laborious task. When facing a data set consisting of more than 1,000 individual microarray experiments, there are endless questions to be asked and hypotheses to be checked.

In our experience, we have found an urgent need for programs that manage large data sets quickly and easily. To this end, some freeware has been developed that clusters and displays data, but there is clearly a need for more user-friendly computer resources for DNA-microarray analysis, particularly for labs that do not specialize in genomics and/or have limited access to computer programmers.

Extracting useful information from transcriptional profiles can be a major challenge. DNA microarray experiments quickly generate millions of data points, a flood of information that can overwhelm biologists.

Should i biology major get a mac or a pc