Over the fall 2018 term, BioBE and ESBL undergrads made a project video explaining a recent publication on the effect of different daylight treatments on bacteria in dust. The post and video can be found here!
I sat down with UO College of Design’s Alex Notman to chat about my work on microbes in buildings and the intersection of biology and buildings: “The Great Indoors: Interior Ecology Under the Looking Glass.”
This fall, I developed and taught a course called Introduction to Mammalian Microbiomes for the University of Oregon Clark Honors College. The course objectives were to:
- introduce students to basic concepts, laboratory techniques, historical background, terminology, and technology related to microbial ecology in or on mammals,
- familiarize students with online resources, including sequence repositories, scientific databases, and analysis tools,
- discuss how host-associated microbiomes are shaped by the anatomy and lifestyle of the host, and how the microbiome can reflect onto the health and performance of the host, and
- review current literature on host-associated microbial ecology.
Keeping it fresh
While I’ve taught similar material at Montana State University, and have plenty of teaching experience from my graduate teaching assistant days at the University of Vermont, I’ve learned that each student population is different, with a unique core knowledge base and interests. Thus, I developed this course from scratch, and constantly revised it during the semester to adjust to the pace and learning style of my students. A draft syllabus, as well as an example of a student’s final project, can be found on my GitHub.
To improve engagement, I tried to make the course (which did not have a lab section) more interactive. I offered a tour of the molecular biology lab I work in, I brought agar plates to class so students could try culturing their own microbiota, and I dressed up like a dead cat.
These students were not science majors, and had had very little science since high school. Even if they had been science majors, I wanted to give a broader look at the field of science than just giving an overview of current knowledge. At the end of some lectures, I facilitated class discussions on various topics in science: the role of scientists in communicating science and whether we should report only or have an obligation to convince the public; elitism, recognition, and credit for intellectual property in a highly-collaborative working environment; the transfer of maternal microbiota and health status to offspring and how we approach prenatal care and parental leave; air quality (and air microbiota), residential zoning in urban areas, and income inequality; should we eat dirt?, etc. The students enthusiastically participated in class discussions, and — to my surprise — requested more (see below).
Phone a friend
I wanted to highlight current research in host-associated microbiomes, and hosted three mini-lectures from guest researchers; Deepika Sundarraman, a graduate students in UO physics, Dr. Candace Williams, a postdoctoral researcher who Skyped in from Vienna, and Dr. Edward Pajarillo, a postdoctoral researcher who Skyped in from Florida.
I really enjoyed teaching this group of students, and I got regular feedback from them about how the course was going and what was working. More formally, I volunteered the class to participate in a pilot evaluation for my midterm and end of term review, which asked more probing questions of students than typical teaching evaluations. For the midterm, only 4 of 15 students responded, but for the final, 13 of 15 responded and I have decided to share those (anonymous) course evaluations for IMM2018:
Students wanted more in-class discussions, and more group-based work, which was surprising to me as science students tend to prefer fewer of these, or at least the option to opt out. I am already considering additional topics for discussion next year. While there was an option on the final to submit a group project, no one chose to pursue that. Similarly, students were able to work collaboratively on journal article summaries to improve their comprehension, provided each student submitted a unique response. Perhaps this option simply needs to be reiterated.
What surprised me most about the evaluations was that several students replied that (the second half of) the course was not challenging enough. The course content was entirely new to them, and while the assignments drew on skills from their core competency as humanities students (reading and writing), they were required to distill large amounts of scientific information and be able to explain it back to me. It’s a challenge to serve the learning speed and style of all students in a class, and I try to manage this by varying the format of assignments, as well as to teach skills in the first part of the class which can be refined with successive assignments.
An example of this was the final project, for which the students needed to create a public outreach presentation in the format of their choice (essay, poster, pamphlet, presentation), which covered a particular topic or discussion point on host-associated microbial communities. Students were able to draw from scientific article summaries they had previously written, or even material from their exams (take-home essays), provided it was more developed and presented in a new and creative way. This flexibility allowed students to choose topics that they were passionate about, and to focus on the message rather the format. I felt this would help them find their voice, and judging by the final projects I received, it was effective.
That being said, if humanities students thought the material too easy, I take credit for communicating it well. I’m pleased with how the course turned out, as well as with the feedback I received from students. I’ve already begun implementing upgrades to my curricula, and have proposed this course again to the Honors College. Pending approval, I’ll be back at it next year!
No one is sorry to say goodbye to 2018, yet it still seems like the 2018 Year in Review has arrived too soon. As usual, I’ve been keeping busy; you can find my reviews for 2017 and 2016 in the archives. For the first year in the
three years since I started this blog, I’m not starting a new job! I’ve been at BioBE for a year and a half, and it’s a relief to be in an academic position long enough to finish the projects you started (I’m only just starting to submit some manuscripts for work I did back in Montana).
Two papers of mine were published this year, including one on the bacteria along the GI tract of calves, one on the effect of dietary zinc on bacteria in sheep. A comprehensive culturing initiative of rumen microorganisms, called the Hungate 1000 Project, an international initiative to which I contributed data, was also published. That puts me up to 17 scientific articles, of which 9 are first-authored, as well as 5 scientific reviews. I have three manuscripts in review right now, and another five being prepared – 2019 will be a busy year.
I joined two journal editorial boards this year, PloS One and Applied and Environmental Microbiology. Both positions are as an Academic (or handling) Editor; I will oversee manuscript review by soliciting reviewers, assessing their recommendations, and interfacing with authors. In recent years, the gender discrepancy in science has received more attention, and some journals are making efforts towards increasing the number of female editors, reviewers, and contributors to reduce implicit bias in science publishing. I am pleased to be in a position where I can help change that!
I’ve been spending a lot of time writing grants and developing potential projects on microbiology and health in the built environment, many of which should be moving forward in 2019. I’ve also been spending time training the 9 undergraduate students I hired over the summer and fall to work at BioBE. In addition to microbiology and molecular biology laboratory skills, I have been training them on DNA sequence analysis and coding, scientific literature review, and science writing and communications.
This fall term, I taught Introduction to Mammalian Microbiomes for the University of Oregon Clark Honor’s College. I proposed this new course last year, and developed the curricula largely from scratch. I’d previously taught some of the subject material at Montana State University in Carl Yeoman and Seth Walk’s Host-Associated Microbiomes course; however in IMM I was teaching to non-science majors. The course went well, and I’ll be diving into it in detail with a full blog post in a few weeks. I proposed the course again for next year, as well as another new course; Microbiology of the Built Environment.
Presentations and travel
Early in the year, I gave two public talks on the gut microbiome for Oregon Museum of Science and Industry; one in Eugene and one in Portland. Both were a lot of fun, and I really enjoyed getting to share my work with the public.
In May, the research group I am part of (the Institute for Health in the Built Environment, comprising the Biology and the Built Environment Center, the Energy Studies in Buildings Laboratory, and Baker Lighting Lab) hosted a mini-conference in Portland in May; the Health and Energy Consortium 2018. I presented some results on how some home factors affect the bacteria community found indoors, as well as brainstormed research ideas with industry professionals and researchers.
At the end of the spring term, I also presented at the University of Oregon IDEAL Framework Showcase. Over the 2017/2018 academic year I served on the Implicit Bias working group, tasked with assessing the need for campus-wide training and making recommendations to the college.
In June, I attended the HOMEChem Open House at the UT Austin Test House, University of Texas at Austin’s J.J. Pickle Research Campus. I got to tour the amazing indoor chemistry labs there, and met with BioBE collaborators to discuss pilot projects exploring the link between indoor chemistry and indoor microbiology.
In July, I had a double header of back-to-back conferences, both of which I was attending for the first time. The first was Microbiology of Built Environment 2018 Gordon Research Conference in Biddeford, ME, followed by Indoor Air 2018 in Philadelphia, PA.
MoBE 2018 was an intensive meeting that brought together the top names and the rising stars of MoBE research. Gordon conferences are closed-session to encourage the presentation of unpublished data and ideas, and to facilitate discussion and theoretical contemplation. While in Biddeford, I had the opportunity to eat seafood, visit friends, and check out Mug Buddy Cookies!!
Immediately after MoBE, I flew to Philadelphia for the Indoor Air 2018 conference. I again presented some of the work I’ve been part of, exploring the effect of weatherization and lifestyle on bacteria indoors. I also found some incredible shoes.
Then, in August I went to Leipzig, Germany for the 17th International Society for Microbial Ecology (ISME17). Here as well, I presented some of the work I’ve been part of, and had the chance to revisit a city I haven’t been to in 5 years – since the last microbial ecology conference held here.
I spent a great deal of 2018 participating in activities for 500 Women Scientists. I am a Pod Coordinator for the Eugene Pod, and as such I meet regularly with other Coordinators to plan events. The majority of our 2018 events were Science Salons: science talks by local female researchers around a particular theme, with a hands-on activity to match, and a Q&A session about life as a (female) scientist. We heard about some awesome research, raised $1300 for local science non-profits, and learned how to be better community members by sharing personal stories about the triumphs and troughs of being a woman in science.
We also hosted a film screening of My Love Affair with the Brain, generously lent to 500WS by Luna Productions, followed by a panel discussion of women neuroscientists here in Eugene.
Along with two other Eugene Pod Coordinators, I wrote a small proposal which was funded, to coordinate workshops at UO: “Amplifying diverse voices: training and support for managing identity-based harassment in science communication”. Those workshops will take place in 2019.
This year, I acted as a judge for several robotics competitions and STEM design projects for local schools, I even dressed up as a giant spider to throw corn starch at campers. You know, for the kids.
I again participated in citizen science through Adventure Scientists, as part of their wood crews for the Timber Tracking 2018 campaign. Lee and I drove around a 20,000 sq mi section of southwestern Oregon to collect samples from big leaf maple trees at 10 locations which adhered to certain sampling parameters. Despite the large number of big leafs in Oregon, the sampling criteria made it difficult to find the perfect tree in an entire forest, and we logged a lot of mileage. Lee and I also volunteered for their Gallatin County Microplastics Initiative while we lived in Bozeman, MT.
I published 30 posts this year! The most popular post this year continues to be Work-Life Balance: What Do Professors Do?, self explanatory, and the least popular this year is Show Me the (Grant) Money, detailing the grant proposal writing process. Although, I was significantly less wordy this year as compared to other years.
As of today, my site received 4,447 view from 97 countries and 3,101 visitors in 2018. So far, I’ve published 109 posts, and received 6,147 visitors who viewed the site 9,481 times.
It’s easy to forget how many life events go by in a year, unless your social media is making you a video about them. But they were all important parts of my life and had some impact, however negligible, on my work. The one I’m most proud of was officiating the wedding of two dear friends, in Vermont.
I tried to spend more time on creative projects, including getting back into art after more-or-less tabling it for several years.
As usual, 2019 promises an abundance of opportunities. Already, I am planning out my conference schedule, seeking speakers for upcoming 500WS Science Salons, and writing, writing, writing. But through all of it, I will be trying to cultivate a more open, inclusive, and supportive work environment. In 2018, after more than a decade of trying to convince doctors that I should have agency over my own organs, I was finally approved for the hysterectomy that I’d wanted for so long, and the medical diagnostics to show that I’d actually needed it for probably just as long.
The surgery has dramatically improved my quality of life, and the scars are a constant reminder that you never know who is dealing with something in their life that isn’t visible to you, who is trying to pretend they aren’t in pain because they can’t afford to take time off to resolve their situation. At first, I kept the details to myself and I kept it off my professional social media. I did share, in exquisite detail, on my personal social media, and was flooded with similar stories from other women. It encouraged me to share a little more, after all, if I’d had surgery on a knee or a kidney I would talk about it openly, why not a uterus?
In a typical semester, one to two-thirds of the students that I teach or mentor will disclose that they experienced a serious life event, most often while at school. They may casually joke about how they couldn’t get time off or almost failed out that semester, or recall how receiving help saved them. I take my role as an educator, mentor, or supervisor seriously – the competition in academia forces students to work long or odd hours, to prioritize other things over study, to accept positions of low or no pay “for the experience”, or to accept professional relationships where they are not respected or may be taken advantage of. I have always tried to be a supportive mentor to students, but the higher up the ladder I climb the more important it is for me to set a good example for these students who will one day mentor people of their own.
In addition to listening to them, and having frank conversations, my response this year has been to get rid of student employee deadlines whenever possible. We are asked to do so much with our time in school, or in academia, but there are so many hours in the day. Sure, I routinely wish things were accomplished more promptly, but I have never once regretted not causing someone to have a breakdown. And constantly telling my students to take care of themselves first and work second reminds me to do the same, it benefits my work , and it’s made a certain furball very happy. Happy New Year!
Last year, one of my former research groups at Montana State University was awarded a USDA NIFA Foundational program grant, and I am a sub-award PI on that grant. We’ll be working together to investigate the effect of diversified farming systems – such as those that use cover crops, rotations, or integrate livestock grazing into field management – on crop production and soil bacterial communities: “Diversifying cropping systems through cover crops and targeted grazing: impacts on plant-microbe-insect interactions, yield and economic returns.”
The first soil samples were collected in Montana this summer, and I have been processing them for the past few weeks. I am using the opportunity to train a master’s student on microbiology and molecular genetics lab work.
Tindall Ouverson started this fall as a master’s student at MSU, working with Fabian Menalled and Tim Seipel in Bozeman, MT. She’s an environmental and soil scientist, and this is her first time working with microbes. She was here in Eugene for just a few days to learn everything needed for sequencing: DNA extraction, polymerase chain reaction, gel electrophoresis and visualization, DNA cleanup using magnetic beads, quantification, and pooling. Despite not having experience in microbiology or molecular biology, Tindall showed a real aptitude and picked up the techniques faster than I expected!
Once the sequences are generated, I’ll be (remotely) training Tindall on DNA sequence analysis. I’ll also be serving as one of her thesis committee members! Tindall will be the first of (hopefully) many cross-trained graduate students between myself and collaborators at MSU.
I’m pleased to announce that a working group composed of myself, and University of Oregon doctoral students Theresa Cheng (Neuroscience) and Deepika Sundarraman (Physics) have been awarded a UO Biology Diversity, Equity and Inclusion Grant!
We have been awarded $1000 to develop a series of professional development workshops for managing identity-based harassment in science communication. Theresa, Deepika, and I are Pod Coordinators for the Eugene Pod of 500 Women Scientists, and we have been looking to expand our repertoire of activities in the Eugene community. This grant will help us reach scientists to promote professional development, diversity, equity, and inclusion!
“Amplifying diverse voices: training and support for managing identity-based harassment in science communication”
Statement of Proposed Activity
Communicating science to the public is professionally challenging, and for researchers from underrepresented communities, public engagement involves overcoming stereotypical perceptions about professional capacity [1,2]. Facing heckling, harassment, or discrimination can alter how researchers engage with the public, as well as their willingness to do so . This reduces the visibility of these scientists and their work, and can stymie their professional development and the public’s perception of scientists [4,5].
To address this, we propose to organize several professional development workshops on campus on overcoming identity-based discrimination and harassment in public engagement for scientists. Our target audience for these events are students and faculty in the UO Department of Biology who self-identify as marginalized or underrepresented in science. In particular, we will recruit recruit undergraduate to early career faculty women in the biological sciences.
The workshops will be presented by Rehearsals for Life (RfL), a social justice graduate student theatre troupe at UO which uses innovative and interactive techniques to engage participants in dialogue. RfL trains attendees to handle difficult situations arising from public communication and engagement in a manner that is sensitive to issues of diversity, equity, and inclusion. These workshops will be tailored by RfL based on audience concerns, e.g., sharing environmental/ecological research to audiences of climate change skeptics. Attendance is capped to support an intimate, safe, tailored, and participatory experience; across two workshops, we will support the development of 80 scientists.
Giving people the tools to engage with the public gives them the confidence to do so, thus promoting the visibility of scientific research from a diverse UO cohort. Additionally, these events will connect women and underrepresented scientists across academic levels to build our campus community. We will evaluate the success of the workshops in accomplishing these goals via a post-event survey that asks about (a) skill development, (b) confidence in public engagement, (c) sense of community, and (d) talks/other instances of public engagement.
1. Catalyst. Women “Take Care,” Men “Take Charge:” Stereotyping of U.S. Business Leaders Exposed [Internet]. Catalyst. 2005. Available: https://www.catalyst.org/knowledge/women-take-care-men-take-charge-stereotyping-us-business-leaders-exposed
3. Phoenix J. An Open Letter to People Who Send Hate Mail to Scientists. In: Medium [Internet]. Medium; 13 Sep 2018 [cited 6 Oct 2018]. Available: https://medium.com/@jessphoenix2018/an-open-letter-to-people-who-send-hate-mail-to-scientists-8b1b6df518cb
5. National Science Board. Science and Engineering Indicators 2014 [Internet]. US National Science Foundation (NSF); 2014. Available: https://www.nsf.gov/statistics/seind14/index.cfm/chapter-7/c7s3.htm
I am pleased to announce I have joined the Applied and Environmental Microbiology journal editorial team! It’s a three-year term, from Jan 2019 to Dec 2021. AEM, established in 1953, is the one of the journals published by the American Society for Microbiology (ASM), of which I have been a member for a number of years.
As I discussed previously, the effort of many individuals goes into a scientific manuscript, including ad-hoc reviewers and editorial staff at journals. As an Editor, I interface between scientific reviewers and higher-level editorial staff to manage the peer-review process; including evaluating manuscript submissions for applicability to the journal, selecting reviewers, and assessing reviewer comments to making editorial decisions on publishing.
Zinc is an important mineral in your diet; it’s required by many of your enzymes and having too much or too little can cause health problems. We know quite a bit about how important zinc is to sheep, in particular for their growth, immune system, and fertility. We also know that organically- versus inorganically-sourced zinc differs in its bio-availability, or how easy it is for cells to access and use it. Surprisingly, we know nothing about how different zinc formulations might affect gut microbiota, despite the knowledge that microorganisms may also need zinc.
This collaborative study was led by Dr. Whit Stewart and his then-graduate student, Chad Page, while they were at Montana State University (they are now both at the University of Wyoming). Chad’s work focused on how different sources of zinc affected sheep growth and performance (previously presented, publication forthcoming), and I put together this companion paper examining the effects on rumen bacteria.
The pre-print is available now for Journal of Animal Science members, and the finished proof should be available soon. JAS is the main publication for the American Society of Animal Science, and one of the flagship journals in the field.
Zinc amino acid supplementation alters yearling ram rumen bacterial communities but zinc sulfate supplementation does not.
Ishaq, S.L., Page, C.M., Yeoman, C.J., Murphy, T.W., Van Emon, M.L., Stewart, W.C. 2018. Journal of Animal Science. Accepted. Article.
Featured Image Source: Wikimedia Commons
Original posting on BioBE.
Sequence data contamination from biological or digital sources can obscure true results and falsely raise one’s hopes. Contamination is a persist issue in microbial ecology, and each experiment faces unique challenges from a myriad of sources, which I have previously discussed. In microbiology, those microscopic stowaways and spurious sequencing errors can be difficult to identify as non-sample contaminants, and collectively they can create large-scale changes to what you think a microbial community looks like.
Samples from large studies are often processed in batches based on how many samples can be processed by certain laboratory equipment, and if these span multiple bottles of reagents, or water-filtration systems, each batch might end up with a unique contamination profile. If your samples are not randomized between batches, and each batch ends up representing a specific time point or a treatment from your experiment, these batch effects can be mistaken for a treatment effect (a.k.a. a false positive).
Due to the high cost of sequencing, and the technical and analytical artistry required for contamination identification and removal, batch effects have long plagued molecular biology and genetics. Only recently have the pathologies of batch effects been revealed in a harsher light, thanks to more sophisticated analysis techniques (examples here and here and here) and projects dedicated to tracking contamination through a laboratory pipeline. To further complicate the issue, sources of and practical responses to contamination in fungal data sets is quite different than that of bacterial data sets.
“The times were statistically greater than prior time periods, while simultaneously being statistically lesser to prior times, according to longitudinal analysis.”
Over the past year, I analyzed a particularly complex bacterial 16S rRNA gene sequence data set, comprising nearly 600 home dust samples, and about 90 controls. Samples were collected from three climate regions in Oregon, over a span of one year, in which homes were sampled before and approximately six weeks after a home-specific weatherization improvement (treatment homes) or simply six weeks later in (comparison) homes which were eligible for weatherization but did not receive it. As these samples were collected over a span of a year, they were extracted with two different sequencing kits and multiple DNA extraction batches, although all within a short time after collection. The extracted DNA was spread across two sequence runs to allow for data processing to begin on cohort 1, while we waited for cohort 2 homes to be weatherized. Thus, there were a lot of opportunities to introduce technical error or biological contamination that could be conflated with treatment effects.
On top of this, each home was unique, with it’s own human and animal occupants, architectural and interior design, plants, compost, and quirks, and we didn’t ask homeowners to modify their behavior in any way. This was important, as it meant each of the homes – and their microbiomes – are somewhat unique. Therefore I didn’t want to remove sequences which might be contaminants on the basis of low abundance and risk removing microbial community members which were specific to that home. After the typical quality assurance steps to curate and process the data, which can be found on GitHub as an R script of a DADA2 package workflow, I needed to decide what to do with the negative controls.
Because sequencing is expensive, most of the time there is only one negative control included in sequencing library preparation, if that. The negative control is a blank sample – just water, or an unused swab – which does not intentionally contain cells or nucleic acids. Thus anything you find there will have come from contamination. The negative control can be used to normalize the relative abundance numbers – if you find 1,000 sequences in the negative control, which is supposed to have no DNA in it, then you might only continue looking at samples with a certain amount higher than 1,000 sequences. This risks throwing out valid sequences that happen to be rare. Alternatively, you can try to identify the contaminants and remove whole taxa from your data set, risking the complete removal of valid taxa.
I had three types of negative controls: sterile DNA swabs which were processed to check for biological contamination in collection materials, kit controls where a blank extraction was run for each batch of extractions to test for biological contamination in extraction reagents, and PCR negative controls to check for DNA contamination of PCR reagents. In total, 90 control samples were sequenced, giving me unprecedented resolution to deal with contamination. Looking at the total number of sequences before and after my quality-analysis processing, I can see that the number of sequences in my negative controls reduces dramatically; they were low-quality in some way and might be sequencing artifacts. But, an unsatisfactory number remain after QA filtering; these are high-quality and likely come from microbial contamination.
I wasn’t sure how I wanted to deal with each type of control. I came up with three approaches, and then looked at unweighted, non-rarefied ordination plots (PCoA) to watch how my axes changed based on important components (factors). What follows is a narrative summarize of what I did, but I included the R script of my phyloseq package workflow and workaround on GitHub.
“In microbial ecology, preprints are posted on late November nights. The foreboding atmosphere of conflated factors makes everyone uneasy.”
Ordination plots visualize lots of complex communities together. In both ordination figures below, each point on the graph represents a dust sample from one house. They are clustered by community distance: those closer together on the plot have a more similar community than points which are further away from each other. The points are shaped by the location of the samples, including Bend, Eugene, Portland, along with a few pilot samples labeled “Out”, and negative controls which have no location (not pictured but listed as NA). The points are colored by DNA extraction b
In Figure 1, the primary axis (axis 1) shows a clear clustering of samples by DNA extraction batch, but this is also mixed with geographic location, and as it turns out – date of collection and sequencing run. We know from other studies that geographic location, date of collection, and sequencing batch can all affect the microbial community.
Approach 1: Subtraction + outright removal
This approach subsets my data into DNA extraction batches, and then uses the number of sequences found in the negative controls to subtract out sequences from my dust samples. This assumes that if a particular sequence showed up 10 times in my negative control, but 50 times in my dust samples, that only 40 of those in my dust sample were real. For each of my DNA extraction batch negative control samples, I obtained the sum of each potential contaminant that I found there, and then subtracted those sums from the same sequence columns in my dust samples.
Approach 1 was alright, but there was still an effect of DNA extraction batch (indicated by color scale) that was stronger than location or treatment (not included on this graph). This approach is also more pertinent for working with OTUs, or situations where you wouldn’t want to remove the whole OTU, just subtract out a certain number sequences from specific columns. There is currently no way to do that just from phyloseq, so I made a work-around (see the GitHub page). However, using DADA2 gives you Sequence Variants, which are more precise and I found it’s better to remove them with approach 3.
Approach 2: Total Removal
This approach removes any contaminant sequences that is found in ANY of the negative controls from ALL the house samples, regardless of which negative control was for which extraction batch. This approach assumes that if it a sequence was found as a contaminant in a negative control somewhere, that it is a contaminant everywhere.
Once again, approach 2 was alright, and now that primary axis (axis 1) of potential batch effect is now my secondary axis; so there is still an effect of DNA extraction batch (indicated by color scale) but it is weaker. When I recolor by different variables, there is much more clustering by Treatment than by any batch effects. However, that second axis is also one of my time variables, so don’t want to get rid of all of the variation on that axis. But, since my negative kit controls showed a lot of variation in number and types of taxa, I don’t want to remove everything found there from all samples indiscriminately.
Additionally, I don’t favor throwing sequences out just because they were a contaminant somewhere, particularly for dust samples. Contamination can be situational, particularly if a microbe is found in the local air or water supply and would be legitimately found in house dust but would have also accidentally gotten into the extraction process.
Approach 3: “To each its own”
This approach removes all the sequences from PCR and swab contaminant SVs fully from each cohort, respectively, and removes extraction kit contaminants fully from each DNA extraction batch, respectively. I took all the sequences of the SVs found in my dust samples and made them into a vector (list), and then I took all the sequences of the SVs found in my controls and made them into a different vector. I effectively subtracted out the contaminant SVs by name, but asking to find the sequences which were different between my two lists (thus returning the sequences which were in my dust samples but not in my control samples). I did this respective to each sequencing cohort and batch, so that I only remove the pertinent sequences (ex. using kit control 1 to subtract from DNA extraction batch 1).
In Figure 4, potential batch effect is solidly my secondary axis and not the primary driving force behind clustering. The primary axis (axis 1) shows a clear separation by climate zone, or location of homes, once the batch contamination has been removed. When I recolor by different variables, there is much more clustering by Treatment and almost none by batch effects. I say almost none, because some of my DNA extraction batches also happen to be Treatment batches, as they represent a subset of samples from a different location. Thus, I can’t tell if those samples cluster separately solely because of location or also because of batch effect. However, I am satisfied with the results and ready to move on.
Unlike its namesake, this tale has a happier ending.
Not a day goes by that I don’t search for information, and whether that information is a movie showtime or the mechanism by which a bacterial species is resistant to zinc toxicity, I need that information to be accurate. In the era of real fake-news and fake real-news, mockumentaries, and misinformation campaigns, the ability to find accurate and unbiased information is more important than ever.
Yet, assessing the validity of information and verifying sources is an under-appreciated and under-taught skill. There are some great resources available for determining the reliability (if the same results are achieved each time), and validity (is it a real effect), of a dataset, as well as of the authors. Even with fact-evaluation resources available through The National Center for Complementary and Integrated Health (NCCIS), The University of Edinburgh, The Georgetown University Library, or Michigan State University, like any skill, finding information takes practice.
Where do I go for Science Information?
Thanks to the massive shift towards digital archiving and open-access online journals, nearly all of my information hunting is done online (and an excellent reason why Net Neutrality is vital to researchers). Most of the time, this information is in the form of scientific journal articles or books online, and finding this information can be accomplished by using regular search engines. In particular, Google has really pushed to improve its ability to index scientific publications (critical to Google Scholar and Paperpile).
However, it takes skill to compose your search request to find accurate results. I nearly always add “journal article” or “scientific study” to the end of my query because I need the original sources of information, not popular media reports on it. This cuts out A LOT of inaccuracy in search results. If I’m looking for more general information, I might add “review” to find scientific papers which broadly summarize the results of dozens to hundreds of smaller studies on a particular topic. If I have no idea where to begin and need basic information on what I’m trying to look for, I will try my luck with a general search online or even Wikipedia (scientists have made a concerted effort to improve many science-related entries). This can help me figure out the right terminology to phrase my question.
How do I know if it’s accurate?
One of the things I’m searching for when looking for accurate sources is peer-review. Typically, scientific manuscripts submitted to reputable journals are reviewed by 1 – 3 other authorities in that field, more if the paper goes through several journal submissions. The reviewers may know who the authors are, but the authors don’t know their reviewers until at least after publication, and sometimes never. This single-blind (or double-blind if the reviewers can’t see the authors’ names) process allows for manuscripts to be reviewed, edited, and challenged before they are published. Note that perspective or opinion pieces in journals are typically not peer-reviewed, as they don’t contain new data, just interpretation. The demand for rapid publishing rates and the rise of predatory journals has led some outlets to publish without peer-review, and I avoid those sources. The reason is that scientists might not see the flaws or errors in their own study, and having a third party question your results improves your ability to communicate those results accurately.
Another way to assess the validity of an article is the inclusion of correct control groups. The control group acts a baseline against which you can measure your treatment effects, those which go through the same experimental parameters except they don’t receive an active treatment. Instead, the group receives a placebo, because you want to make sure that the acts of experimentation and observation themselves do not lead to a reaction – The Placebo Effect. The Placebo Effect is a very real thing and can really throw off your results when working with humans.
Similarly, one study does not a scientific law make. Scientific results can be situational, or particular to the parameters in that study, and might not be generalizable (applicable to a broader audience or circumstances). It often takes dozens if not a hundred studies to get at the underlying mechanisms of an experimental effect, or to show that the effect is reliably recreated across experiments.
Data or it didn’t happen. I can’t stress this one enough. Making a claim, statement, or conclusion is hollow until you have supplied observations to prove it. This a really common problem in internet-based arguments, as people put forth references as fact when they are actually opinionated speeches or videos that don’t list their sources. These opinionated speeches have their place, I post a lot of them myself. They often say what I want to say in a much more eloquent manner. Unfortunately, they are not data and can’t prove your point.
The other reason you need data to match your statements is that in almost all scientific articles, the authors include speculation and theory of thought in the Discussion section. This is meant to provide context to the study, or ponder over the broader meaning, or identify things which need to be verified in future studies. But often these statements are repeated in other articles as if they were facts which were evaluated in the first article, and the ideas get perpetuated as proven facts instead of as theories to be tested. This often happens when the Discussion section of an article is hidden behind a pay wall and you end up taking that second paper’s word for it about what happened in the first paper. It’s only when the claim is traced all the way back to the original article that you find that someone mistook thought supposition for data exposition.
The “Echo Chamber Effect” is also prominent when it comes to translating scientific articles into news publications, a great example of which is discussed by 538. Researchers mapped the genome of about 30 transgender individuals – about half and half of male to female and female to male, to get an idea of whether gender identity could be described with a nuanced genetic fingerprint rather than a binary category. This is an extremely small sample group, and the paper was more about testing the idea and suggesting some genes which would be used for the fingerprint. In the mix-up, comments about the research were attributed to a journalist at 538 – comments that the journalist had not made, and this error was perpetuated when further news organizations used other news publications as the source instead of conducting their own interview or referencing the publication. In addition, the findings and impact of the study were wrongly reported – it was stated that 7 genes had been identified by researchers as your gender fingerprint, which is a gross exaggeration of what the original research article was really about. When possible, try to trace information back to its origin, and get comments straight from the source.
How do I know if it’s unbiased?
This can be tricky, as there are a number of ways someone can have a conflict of interest. One giveaway is tone, as scientific texts are supposed to remain neutral. You can also check the author affiliations (who they are and what institution they are at), the conflict of interest section, and the disclosure of funding source or acknowledgements sections, all of which are common inclusions on scientific papers. “Following the money” is a particularly good way of determining if there is biased involved, depending on the reputation of the publisher.
When in doubt, try asking a librarian
There are a lot of resources online and in-person to help you find accurate information, and public libraries and databases are free to use!