Modern high-throughput biological assays let us ask detailed questions about how diseases operate, and promise to let us personalize therapy. Careful data processing is essential, because our intuition about what the answers "should" look like is very poor when we have to juggle thousands of things at once. When documentation of such processing is absent, we must apply "forensic bioinformatics" to work from the raw data and reported results to infer what the methods must have been. We will present several case studies where simple errors may have put patients at risk. This work has been covered in both the scientific and lay press, including the front page of the New York Times and 60 Minutes, and has prompted several journals to revisit the types of information that must accompany publications. We discuss steps we take to avoid such errors, and lessons that can be applied to large data sets more broadly.