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<section><h3>We seek to automate the process of reading gel doc slides and give reliable band sizes as outputs in a comprehensible format with an easy-to-use software. A manual mode is also developed for graphical analysis to ease detection of fainter bands that are too close to each other to differentiate using ones eyes. The software requires minimum resources: a few Python modules, and can be adjusted to suit any gel doc machine and a variety of standard ladders. The main difficulty in differentiating very closely spaced bands, due to the inefficiency of human eyes, is done away with in this module. Furthermore, we are also working on introducing machine learning in the software to give better accuracy in this regard and also to detect extremely faint bands. The software will help students and researchers to automate the mundane and routine task of reading gel doc slides.</h3> | <section><h3>We seek to automate the process of reading gel doc slides and give reliable band sizes as outputs in a comprehensible format with an easy-to-use software. A manual mode is also developed for graphical analysis to ease detection of fainter bands that are too close to each other to differentiate using ones eyes. The software requires minimum resources: a few Python modules, and can be adjusted to suit any gel doc machine and a variety of standard ladders. The main difficulty in differentiating very closely spaced bands, due to the inefficiency of human eyes, is done away with in this module. Furthermore, we are also working on introducing machine learning in the software to give better accuracy in this regard and also to detect extremely faint bands. The software will help students and researchers to automate the mundane and routine task of reading gel doc slides.</h3> | ||
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+ | <section><center><table> | ||
+ | <tr><td><img src="https://static.igem.org/mediawiki/2017/6/61/T--IISER-Mohali-INDIA--softwreum.png" alt="User Manual" width="100%"></td></tr></table></center></section> | ||
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Revision as of 11:44, 1 November 2017